Deep learning to refine the identification of high-quality clinical research articles from the biomedical literature: Performance evaluation.

BACKGROUND: Identifying practice-ready evidence-based journal articles in medicine is a challenge due to the sheer volume of biomedical research publications. Newer approaches to support evidence discovery apply deep learning techniques to improve the efficiency and accuracy of classifying sound evidence. OBJECTIVE: To determine how well deep learning models using variants of Bidirectional Encoder Representations from Transformers (BERT) identify high-quality evidence with high clinical relevance from the biomedical literature for consideration in clinical practice. METHODS: We fine-tuned variations of BERT models (BERTBASE, BioBERT, BlueBERT, and PubMedBERT) and compared their performance in classifying articles based on methodological quality criteria. The dataset used for fine-tuning models included titles and abstracts of >160,000 PubMed records from 2012 to 2020 that were of interest to human health which had been manually labeled based on meeting established critical appraisal criteria for methodological rigor. The data was randomly divided into 80:10:10 sets for training, validating, and testing. In addition to using the full unbalanced set, the training data was randomly undersampled into four balanced datasets to assess performance and select the best performing model. For each of the four sets, one model that maintained sensitivity (recall) at ≥99% was selected and were ensembled. The best performing model was evaluated in a prospective, blinded test and applied to an established reference standard, the Clinical Hedges dataset. RESULTS: In training, three of the four selected best performing models were trained using BioBERTBASE. The ensembled model did not boost performance compared with the best individual model. Hence a solo BioBERT-based model (named DL-PLUS) was selected for further testing as it was computationally more efficient. The model had high recall (>99%) and 60% to 77% specificity in a prospective evaluation conducted with blinded research associates and saved >60% of the work required to identify high quality articles. CONCLUSIONS: Deep learning using pretrained language models and a large dataset of classified articles produced models with improved specificity while maintaining >99% recall. The resulting DL-PLUS model identifies high-quality, clinically relevant articles from PubMed at the time of publication. The model improves the efficiency of a literature surveillance program, which allows for faster dissemination of appraised research.

Assessment of Thermal and Hydrolytic Stabilities and Aqueous Solubility of Artesunate for Formulation Studies.

For the purpose of establishing the optimum processing parameters and storage conditions associated with nanolipid formulations of the artemisinin derivative artesunate, it was necessary to evaluate the thermal stability and solubility profiles of artesunate in aqueous solutions at various temperatures and pH. The effect of increased temperature and humidity on artesunate was determined by storing samples of the raw material in a climate chamber for 3 months and analyzing these by an established HPLC method. Artesunate remained relatively stable during storage up to 40°C ± 0.5°C and 75% relative humidity for 3 months, wherein it undergoes approximately 9% decomposition. At higher temperatures, substantially greater decomposition supervenes, with formation of dihydroartemisinin (DHA) and other products. In solution, artesunate is relatively stable at 15°C with less than 10% degradation over 24 h. The aqueous solubility of artesunate at different pH values after 60 min are pH 1.2 (0.1 M HCl) 0.26 mg/mL, pH 4.5 (acetate buffer) 0.92 mg/mL, distilled water 1.40 mg/mL, and pH 6.8 (phosphate buffer) 6.59 mg/mL, thus relating to the amount of ionized drug present. Overall, for optimal preparation and storage of the designated formulations of artesunate, relatively low temperatures will have to be maintained throughout.

Preliminary comparison of the performance of the National Library of Medicine's systematic review publication type and the sensitive clinical queries filter for systematic reviews in PubMed.

OBJECTIVE: The National Library of Medicine (NLM) inaugurated a "publication type" concept to facilitate searches for systematic reviews (SRs). On the other hand, clinical queries (CQs) are validated search strategies designed to retrieve scientifically sound, clinically relevant original and review articles from biomedical literature databases. We compared the retrieval performance of the SR publication type (SR[pt]) against the most sensitive CQ for systematic review articles (CQrs) in PubMed. METHODS: We ran date-limited searches of SR[pt] and CQrs to compare the relative yield of articles and SRs, focusing on the differences in retrieval of SRs by SR[pt] but not CQrs (SR[pt] NOT CQrs) and CQrs NOT SR[pt]. Random samples of articles retrieved in each of these comparisons were examined for SRs until a consistent pattern became evident. RESULTS: For SR[pt] NOT CQrs, the yield was relatively low in quantity but rich in quality, with 79% of the articles being SRs. For CQrs NOT SR[pt], the yield was high in quantity but low in quality, with only 8% being SRs. For CQrs AND SR[pt], the quality was highest, with 92% being SRs. CONCLUSIONS: We found that SR[pt] had high precision and specificity for SRs but low recall (sensitivity), whereas CQrs had much higher recall. SR[pt] OR CQrs added valid SRs to the CQrs yield at low cost (i.e., added few non-SRs). For searches that are intended to be exhaustive for SRs, SR[pt] can be added to existing sensitive search filters.

Long-term safety and efficacy of anacetrapib in patients with atherosclerotic vascular disease.

AIMS: REVEAL was the first randomized controlled trial to demonstrate that adding cholesteryl ester transfer protein inhibitor therapy to intensive statin therapy reduced the risk of major coronary events. We now report results from extended follow-up beyond the scheduled study treatment period. METHODS AND RESULTS: A total of 30 449 adults with prior atherosclerotic vascular disease were randomly allocated to anacetrapib 100 mg daily or matching placebo, in addition to open-label atorvastatin therapy. After stopping the randomly allocated treatment, 26 129 survivors entered a post-trial follow-up period, blind to their original treatment allocation. The primary outcome was first post-randomization major coronary event (i.e. coronary death, myocardial infarction, or coronary revascularization) during the in-trial and post-trial treatment periods, with analysis by intention-to-treat. Allocation to anacetrapib conferred a 9% [95% confidence interval (CI) 3-15%; P = 0.004] proportional reduction in the incidence of major coronary events during the study treatment period (median 4.1 years). During extended follow-up (median 2.2 years), there was a further 20% (95% CI 10-29%; P < 0.001) reduction. Overall, there was a 12% (95% CI 7-17%, P < 0.001) proportional reduction in major coronary events during the overall follow-up period (median 6.3 years), corresponding to a 1.8% (95% CI 1.0-2.6%) absolute reduction. There were no significant effects on non-vascular mortality, site-specific cancer, or other serious adverse events. Morbidity follow-up was obtained for 25 784 (99%) participants. CONCLUSION: The beneficial effects of anacetrapib on major coronary events increased with longer follow-up, and no adverse effects emerged on non-vascular mortality or morbidity. These findings illustrate the importance of sufficiently long treatment and follow-up duration in randomized trials of lipid-modifying agents to assess their full benefits and potential harms. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN) 48678192; ClinicalTrials.gov No. NCT01252953; EudraCT No. 2010-023467-18.

A Deep Learning Approach to Refine the Identification of High-Quality Clinical Research Articles From the Biomedical Literature: Protocol for Algorithm Development and Validation.

BACKGROUND: A barrier to practicing evidence-based medicine is the rapidly increasing body of biomedical literature. Use of method terms to limit the search can help reduce the burden of screening articles for clinical relevance; however, such terms are limited by their partial dependence on indexing terms and usually produce low precision, especially when high sensitivity is required. Machine learning has been applied to the identification of high-quality literature with the potential to achieve high precision without sacrificing sensitivity. The use of artificial intelligence has shown promise to improve the efficiency of identifying sound evidence. OBJECTIVE: The primary objective of this research is to derive and validate deep learning machine models using iterations of Bidirectional Encoder Representations from Transformers (BERT) to retrieve high-quality, high-relevance evidence for clinical consideration from the biomedical literature. METHODS: Using the HuggingFace Transformers library, we will experiment with variations of BERT models, including BERT, BioBERT, BlueBERT, and PubMedBERT, to determine which have the best performance in article identification based on quality criteria. Our experiments will utilize a large data set of over 150,000 PubMed citations from 2012 to 2020 that have been manually labeled based on their methodological rigor for clinical use. We will evaluate and report on the performance of the classifiers in categorizing articles based on their likelihood of meeting quality criteria. We will report fine-tuning hyperparameters for each model, as well as their performance metrics, including recall (sensitivity), specificity, precision, accuracy, F-score, the number of articles that need to be read before finding one that is positive (meets criteria), and classification probability scores. RESULTS: Initial model development is underway, with further development planned for early 2022. Performance testing is expected to star in February 2022. Results will be published in 2022. CONCLUSIONS: The experiments will aim to improve the precision of retrieving high-quality articles by applying a machine learning classifier to PubMed searching. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/29398.

Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant Evidence From the Biomedical Literature: Systematic Review.

BACKGROUND: The rapid growth of the biomedical literature makes identifying strong evidence a time-consuming task. Applying machine learning to the process could be a viable solution that limits effort while maintaining accuracy. OBJECTIVE: The goal of the research was to summarize the nature and comparative performance of machine learning approaches that have been applied to retrieve high-quality evidence for clinical consideration from the biomedical literature. METHODS: We conducted a systematic review of studies that applied machine learning techniques to identify high-quality clinical articles in the biomedical literature. Multiple databases were searched to July 2020. Extracted data focused on the applied machine learning model, steps in the development of the models, and model performance. RESULTS: From 3918 retrieved studies, 10 met our inclusion criteria. All followed a supervised machine learning approach and applied, from a limited range of options, a high-quality standard for the training of their model. The results show that machine learning can achieve a sensitivity of 95% while maintaining a high precision of 86%. CONCLUSIONS: Machine learning approaches perform well in retrieving high-quality clinical studies. Performance may improve by applying more sophisticated approaches such as active learning and unsupervised machine learning approaches.

"Evidence-based checklists" for identifying predatory journals have not been assessed for reliability or validity: An analysis and proposal for moving forward.

BACKGROUND: Predatory journals (PJs) pose a threat to the quality/integrity of scientific publishing. Checklists have been proposed to identify PJs, but few are "evidence-based". This study's objective was to search for/assess evidence-based checklists (EBCs) for reliability and validity, based on a new consensus definition of PJs. METHODS: A published methods guideline for scale development was used to identify how many steps were completed in the generation of identified EBCs. Items from each EBC were compared against the consensus definition, and a list of items to be considered in the creation of a composite EBC to identify PJs was generated. RESULTS: Four EBCs were identified. None of these had completed the first of the nine steps for scale development and validation. Forty-seven items from the four EBCs were assessed against the consensus definition, of which 28 items fell within the definition. A proposed composite EBC was created from items matching components of the consensus definition. CONCLUSION: EBCs to detect PJs lack assessment of reliability and validity. To a varying degree, the EBCs contain items that match the scope of the new consensus definition of PJs. With the recent consensus definition, EBCs creators now have a clearer target, and can make adjustments.

Does conventional early life academic excellence predict later life scientific discovery? An assessment of the lives of great medical innovators.

BACKGROUND: Perhaps, as never before, we need innovators. With our growing population numbers, and with increasing pressures on our education systems, are we in danger of becoming more rigid and formulaic and increasingly inhibiting innovation? When young can we predict who will become the great innovators? For example, in medicine, who will change clinical practice? AIMS: We therefore determined to assess whether the current academic excellence approach to medical school entrance would have captured previous great innovators in medicine, assuming that they should all have well fulfilled current entrance requirements. METHODS: The authors assembled a list of 100 great medical innovators which was then approved, rejected or added to by a jury of 12 MD fellows of the Royal Society of Canada. Two reviewers, who had taken both the past and present Medical College Admission Test as part of North American medical school entrance requirements, independently assessed each innovator's early life educational history in order to predict the innovator's likely success at medical school entry, assuming excellence in all entrance requirements. RESULTS: Thirty-one percent of the great medical innovators possessed no medical degree and 24% would likely be denied entry to medical school by today's standards (e.g. had a history of poor performance, failure, dropout or expulsion) with only 24% being guaranteed entry. Even if excellence in only one topic was required, the figure would only rise to 41% certain of medical school entry. CONCLUSION: These data show that today's medical school entry standards would have barred many great innovators and raise questions about whether we are losing medical innovators as a consequence. Our findings have important implications for promoting flexibility and innovation for medical education, and for promoting an environment for innovation in general.

An unusual presentation of propylthiouracil-induced anti-MPO and PR3 positive ANCA vasculitis with associated anti-GBM antibodies, IgA nephropathy and an IgG4 interstitial infiltrate: a case report.

BACKGROUND: A number of disease processes can culminate in rapidly progressive glomerulonephritis, including pauci-immune focal segmental necrotising glomerulonephritis, usually seen with positive serum antineutrophil cytoplasmic antibodies (ANCA). Propylthiouracil (PTU) has been associated with drug-induced ANCA-associated vasculitis (AAV), with antibodies against myeloperoxidase (MPO) and proteinase 3 (PR3) present individually and together having been recognised. 'Double-positive' vasculitis with ANCA and anti-glomerular basement membrane (GBM) antibodies has also been reported in association with PTU treatment. We present a case of PTU-induced anti-MPO and PR3 positive ANCA vasculitis with associated anti-GBM antibodies, IgA nephropathy and an IgG4 interstitial infiltrate. CASE PRESENTATION: A 51-year-old man presented 2 weeks after re-commencing propylthiouracil (PTU) treatment for Graves' disease, with a severe acute kidney injury and haemato-proteinuria. He demonstrated positive titres for autoantibodies to PR3 (76.9 IU/mL), MPO (28.8 IU/mL) and GBM (94 IU/mL). Renal biopsy demonstrated numerous glomerular crescents, widespread IgG4-positive lymphoplasmacytic infiltrate and mesangial positivity for IgA. PTU was stopped and he was treated with steroids, plasma exchange and cyclophosphamide with sustained improvement in his renal function. CONCLUSIONS: This case of drug-induced AAV presented a unique and intriguing collection of serological and histological features. We propose that the PTU-induced AAV resulted in epiphenomena of anti-GBM antibody production and an IgG4-cell-rich tubulointerstitial infiltrate. It is uncertain whether the mesangial IgA deposition preceded or resulted from the AAV.

Context and Approach in Reporting Evaluations of Electronic Health Record-Based Implementation Projects.

Electronic health records (EHRs) are ubiquitous yet still evolving, resulting in a moving target for determining the effects of context (features of the work environment, such as organization, payment systems, user training, and roles) on EHR implementation projects. Electronic health records have become instrumental in effecting quality improvement innovations and providing data to evaluate them. However, reports of studies typically fail to provide adequate descriptions of contextual details to permit readers to apply the findings. As for any evaluation, the quality of reporting is essential to learning from, and disseminating, the results. Extensive guidelines exist for reporting of virtually all types of applied health research, but they are not tailored to capture some contextual factors that may affect the outcomes of EHR implementations, such as attitudes toward implementation, format and amount of training, post go-live support, amount of local customization, and time diverted from direct interaction with patients to computers. Nevertheless, evaluators of EHR-based innovations can choose reporting guidelines that match the general purpose of their evaluation and the stage of their investigation (planning, protocol, execution, and analysis) and should report relevant contextual details (including, if pertinent, any pressures to help justify the huge investments and many years required for some implementations). Reporting guidelines are based on the scientific principles and practices that underlie sound research and should be consulted from the earliest stages of planning evaluations and onward, serving as guides for how evaluations should be conducted as well as reported.

Development and validation of study filters for identifying controlled non-randomized studies in PubMed and Ovid MEDLINE.

A retrospective analysis published by the German Institute for Quality and Efficiency in Health Care (IQWiG) in 2018 concluded that no filter for non-randomized studies (NRS) achieved sufficient sensitivity (≥92%), a precondition for comprehensive information retrieval. New NRS filters are therefore required, taking into account the challenges related to this study type. Our evaluation focused on the development of study filters for NRS with a control group ("controlled NRS"), as this study type allows the calculation of an effect size. In addition, we assumed that due to the more explicit search syntax, controlled NRS are easier to identify than non-controlled ones, potentially resulting in better performance measures of study filters for controlled NRS. Our aim was to develop study filters for identifying controlled NRS in PubMed and Ovid MEDLINE. We developed two new search filters that can assist clinicians and researchers in identifying controlled NRS in PubMed and Ovid MEDLINE. The reference set was based on 2110 publications in Medline extracted from 271 Cochrane reviews and on 4333 irrelevant references. The first filter maximizes sensitivity (92.42%; specificity 79.67%, precision 68.49%) and should be used when a comprehensive search is needed. The second filter maximizes specificity (92.06%; precision 82.98%, sensitivity 80.94%) and should be used when a more focused search is sufficient.

An evidence rating service provided valid correlates of the clinical importance of medical articles and journals.

OBJECTIVES: The objective of this study was to determine reliability and validity of McMaster PLUS measures of scientific merit and clinical importance of articles in medical journals. STUDY DESIGN AND SETTING: Analytic survey of peer-reviewed medical journals was carried out. Articles were qualified for inclusion by meeting (1) scientific criteria and (2) a clinical importance rating threshold. Included articles were sent as e-mail alerts to physicians according to their clinical interests. Internal measures included the number of high-quality, clinically important studies published in source journals and response to alerts. For external validation, we correlated internal measures with the Journal Impact Factor (JIF) and citation in DynaMed Plus (DMP). RESULTS: We evaluated 34,232 articles from 57 journals. Inclusion criteria were met by 2,638 articles (7.71%). The number of qualifying articles per journal was correlated with the number of articles with high clinical importance ratings (r 0.96, P < 0.001), article alert clicks (r 0.86, P < 0.001), and DMP citations (r 0.99, P < 0.001). Correlation was lower with the JIF (r 0.68, P < 0.01). CONCLUSIONS: Measures of scientific merit and clinical importance of medical journal articles were strongly correlated with each other, less so with JIFs. Journals varied widely by these measures but, generally, few articles were both scientifically sound and clinically important.

Effect of Patient-Centered Transitional Care Services on Clinical Outcomes in Patients Hospitalized for Heart Failure: The PACT-HF Randomized Clinical Trial.

Importance: Health care services that support the hospital-to-home transition can improve outcomes in patients with heart failure (HF). Objective: To test the effectiveness of the Patient-Centered Care Transitions in HF transitional care model in patients hospitalized for HF. Design, Setting, and Participants: Stepped-wedge cluster randomized trial of 2494 adults hospitalized for HF across 10 hospitals in Ontario, Canada, from February 2015 to March 2016, with follow-up until November 2016. Interventions: Hospitals were randomized to receive the intervention (n = 1104 patients), in which nurse-led self-care education, a structured hospital discharge summary, a family physician follow-up appointment less than 1 week after discharge, and, for high-risk patients, structured nurse homevisits and heart function clinic care were provided to patients, or usual care (n = 1390 patients), in which transitional care was left to the discretion of clinicians. Main Outcomes and Measures: Primary outcomes were hierarchically ordered as composite all-cause readmission, emergency department (ED) visit, or death at 3 months; and composite all-cause readmission or ED visit at 30 days. Secondary outcomes were B-PREPARED score for discharge preparedness (range: 0 [most prepared] to 22 [least prepared]); the 3-Item Care Transitions Measure (CTM-3) for quality of transition (range: 0 [worst transition] to 100 [best transition]); the 5-level EQ-5D version (EQ-5D-5L) for quality of life (range: 0 [dead] to 1 [full health]); and quality-adjusted life-years (QALY; range: 0 [dead] to 0.5 [full health at 6 months]). Results: Among eligible patients, all 2494 (mean age, 77.7 years; 1258 [50.4%] women) completed the trial. There was no significant difference between the intervention and usual care groups in the first primary composite outcome (545 [49.4%] vs 698 [50.2%] events, respectively; hazard ratio [HR], 0.99 [95% CI, 0.83-1.19]) or in the second primary composite outcome (304 [27.5%] vs 408 [29.3%] events, respectively; HR, 0.93 [95% CI, 0.73-1.18]). There were significant differences between the intervention and usual care groups in the secondary outcomes of mean B-PREPARED score at 6 weeks (16.6 vs 13.9; difference, 2.65 [95% CI, 1.37-3.92]; P < .001); mean CTM-3 score at 6 weeks (76.5 vs 70.3; difference, 6.16 [95% CI, 0.90-11.43]; P = .02); and mean EQ-5D-5L score at 6 weeks (0.7 vs 0.7; difference, 0.06 [95% CI, 0.01 to 0.11]; P = .02) and 6 months (0.7 vs 0.6; difference, 0.06 [95% CI, 0.01-0.12]; P = .02). There was no significant difference in mean QALY between groups at 6 months (0.3 vs 0.3; difference, 0.00 [95% CI, -0.02 to 0.02]; P = .98). Conclusions and Relevance: Among patients with HF in Ontario, Canada, implementation of a patient-centered transitional care model compared with usual care did not improve a composite of clinical outcomes. Whether this type of intervention could be effective in other health care systems or locations would require further research. Trial Registration: ClinicalTrials.gov Identifier: NCT02112227.

A Systematic Review of Medication Adherence Thresholds Dependent of Clinical Outcomes.

Background: In pharmacotherapy, the achievement of a target clinical outcome requires a certain level of medication intake or adherence. Based on Haynes's early empirical definition of sufficient adherence to antihypertensive medications as taking ≥80% of medication, many researchers used this threshold to distinguish adherent from non-adherent patients. However, we propose that different diseases, medications and patient's characteristics influence the cut-off point of the adherence rate above which the clinical outcome is satisfactory (thereafter medication adherence threshold). Moreover, the assessment of adherence and clinical outcomes may differ greatly and should be taken into consideration. To our knowledge, very few studies have defined adherence rates linked to clinical outcomes. We aimed at investigating medication adherence thresholds in relation to clinical outcomes. Method: We searched for studies that determined the relationship between adherence rates and clinical outcomes in the databases PubMed, EmbaseⓇ and Web of Science™ until December 2017, limited to English-language. Our outcome measure was any threshold value of adherence. The inclusion criteria of the retrieved studies were (1) any measurement of medication adherence, (2) any assessment of clinical outcomes, and (3) any method to define medication adherence thresholds in relation to clinical outcomes. We excluded articles considered as a tutorial. Two authors (PB and IA) independently screened titles and abstracts for relevance, reviewed full-texts, and extracted items. The results of the included studies are presented qualitatively. Result: We analyzed 6 articles that assessed clinical outcomes linked to adherence rates in 7 chronic disease states. Medication adherence was measured with Medication Possession Ratio (MPR, n = 3), Proportion of Days Covered (PDC, n = 1), both (n = 1), or Medication Event Monitoring System (MEMS). Clinical outcomes were event free episodes, hospitalization, cortisone use, reported symptoms and reduction of lipid levels. To find the relationship between the targeted clinical outcome and adherence rates, three studies applied logistic regression and three used survival analysis. Five studies defined adherence thresholds between 46 and 92%. One study confirmed the 80% threshold as valid to distinguish adherent from non-adherent patients. Conclusion: The analyzed studies were highly heterogeneous, predominantly concerning methods of calculating adherence. We could not compare studies quantitatively, mostly because adherence rates could not be standardized. Therefore, we cannot reject or confirm the validity of the historical 80% threshold. Nevertheless, the 80% threshold was clearly questioned as a general standard.

A Deep Learning Method to Automatically Identify Reports of Scientifically Rigorous Clinical Research from the Biomedical Literature: Comparative Analytic Study.

BACKGROUND: A major barrier to the practice of evidence-based medicine is efficiently finding scientifically sound studies on a given clinical topic. OBJECTIVE: To investigate a deep learning approach to retrieve scientifically sound treatment studies from the biomedical literature. METHODS: We trained a Convolutional Neural Network using a noisy dataset of 403,216 PubMed citations with title and abstract as features. The deep learning model was compared with state-of-the-art search filters, such as PubMed's Clinical Query Broad treatment filter, McMaster's textword search strategy (no Medical Subject Heading, MeSH, terms), and Clinical Query Balanced treatment filter. A previously annotated dataset (Clinical Hedges) was used as the gold standard. RESULTS: The deep learning model obtained significantly lower recall than the Clinical Queries Broad treatment filter (96.9% vs 98.4%; P<.001); and equivalent recall to McMaster's textword search (96.9% vs 97.1%; P=.57) and Clinical Queries Balanced filter (96.9% vs 97.0%; P=.63). Deep learning obtained significantly higher precision than the Clinical Queries Broad filter (34.6% vs 22.4%; P<.001) and McMaster's textword search (34.6% vs 11.8%; P<.001), but was significantly lower than the Clinical Queries Balanced filter (34.6% vs 40.9%; P<.001). CONCLUSIONS: Deep learning performed well compared to state-of-the-art search filters, especially when citations were not indexed. Unlike previous machine learning approaches, the proposed deep learning model does not require feature engineering, or time-sensitive or proprietary features, such as MeSH terms and bibliometrics. Deep learning is a promising approach to identifying reports of scientifically rigorous clinical research. Further work is needed to optimize the deep learning model and to assess generalizability to other areas, such as diagnosis, etiology, and prognosis.

Knowledge to action: Rationale and design of the Patient-Centered Care Transitions in Heart Failure (PACT-HF) stepped wedge cluster randomized trial.

INTRODUCTION: Heart Failure (HF) is a common cause of hospitalization in older adults. The transition from hospital to home is high-risk, and gaps in transitional care can increase the risk of re-hospitalization and death. Combining health care services supported by meta-analyses, we designed the PACT-HF transitional care model. METHODS: Adopting an integrated Knowledge Translation (iKT) approach in which decision-makers and clinicians are partners in research, we implement and test the effectiveness of PACT-HF among patients hospitalized for HF. We use a pragmatic stepped wedge cluster randomized trial design to introduce the complex health service intervention to 10 large hospitals in a randomized sequence until all hospitals initiate the intervention. The goal is for all patients hospitalized with HF to receive self-care education, multidisciplinary care, and early follow-up with their health care providers; and in addition, for high-risk patients to receive post-discharge nurse-led home visits and outpatient care in Heart Function clinics. This requires integration of care across hospitals, home care agencies, and outpatient clinics in our publicly funded health care system. While hospitals are the unit of recruitment and analysis, patients (estimated sample size of 3200) are the unit of analysis. Primary outcomes are hierarchically ordered as time to composite all-cause readmissions / emergency department (ED) visits / death at 3 months and time to composite all-cause readmissions / ED visits at 30 days. In a nested study of 8 hospitals, we measure the patient-centered outcomes of Discharge Preparedness, Care Transitions Quality, and Quality Adjusted Life Years (QALY); and the 6-month health care resource use and costs. We obtain all clinical and cost outcomes via linkages to provincial administrative databases. CONCLUSIONS: This protocol describes the implementation and testing of a transitional care model comprising health care services informed by high-level evidence. The study adopts an iKT and pragmatic approach, uses a robust study design, links clinical trial data with outcomes held in administrative databases, and includes patient-reported outcomes. Findings will have implications on clinical practice, health care policy, and Knowledge Translation (KT) research methodology.

The Artemisinin Derivative Artemisone Is a Potent Inhibitor of Human Cytomegalovirus Replication.

Human cytomegalovirus (HCMV) is a major cause of disease in immunocompromised individuals and the most common cause of congenital infection and neurosensorial disease. The expanding target populations for HCMV antiviral treatment along with the limitations of the currently available HCMV DNA polymerase inhibitors underscore the need for new antiviral agents with alternative modes of action. The antimalarial artemisinin derivative artesunate was shown to inhibit HCMV in vitro yet has demonstrated limited antiviral efficacy in vivo, prompting our search for more potent anti-HCMV artemisinin derivatives. Here we show that the innovative artemisinin derivative artemisone, which has been screened for its activity against malaria parasites in human clinical studies, is a potent and noncytotoxic inhibitor of HCMV. Artemisone exhibited an antiviral efficacy comparable to that of ganciclovir (50% effective concentration, 1.20 ± 0.46 µM) in human foreskin fibroblasts, with enhanced relative potency in lung fibroblasts and epithelial cells. Significantly, the antiviral efficacy of artemisone was consistently ≥10-fold superior to that of artesunate in all cells. Artemisone effectively inhibited both laboratory-adapted and low-passage-number clinical strains, as well as drug-resistant HCMV strains. By using quantitative viral kinetics and gene expression studies, we show that artemisone is a reversible inhibitor targeting an earlier phase of the viral replication cycle than ganciclovir. Importantly, artemisone most effectively inhibited HCMV infection ex vivo in a clinically relevant multicellular model of integral human placental tissues maintained in organ culture. Our promising findings encourage preclinical and clinical studies of artemisone as a new inhibitor against HCMV.

Potential use of two filter media in constructed wetlands for simultaneous removal of As, V and Mo from alkaline wastewater - Batch adsorption and column studies.

The potential to use water treatment sludge and bauxite as active filter media in constructed wetlands to remove As, V and Mo from alkaline drainage originating from seawater-neutralized bauxite processing residue was evaluated in laboratory batch and column studies. Batch adsorption studies showed that increasing the electrolyte concentration from 0.01 to 0.30 M NaCl (the typical electrolyte strength of the drainage) increased adsorption of all three oxyanions onto both media while increasing initial pH from 6.7 to 8.3 (the typical pH of drainage) and using granules (1-2 mm dia.) rather than ground material (<0.2 mm) both decreased adsorption. Kinetic studies showed that while ionic strength had little effect on the contact time required to reach maximum adsorption, increased initial pH increased the time to reach maximum adsorption for Mo on both media and increased particle size increased the time required for maximum adsorption of all three oxyanions onto both media. In batch experiments, at initial elemental concentrations of 1 and 50 mg L-1, adsorption from multi-element solutions (compared with single element ones) was reduced in the order: Mo ¼â€¯As > V. In continuous flow column studies from single element solutions (1 mg L-1), breakthrough curves for Mo occurred first and greater than three times more eluent passed through the columns before breakthrough of V and then As occurred. When multi-element solutions were used, less volume of eluent was required for breakthrough of all three anions and the volume required before breakthrough of As was greatly reduced compared to that for V. The possibility that the strong ability of V to compete with As and particularly Mo could cause desorption of previously adsorbed Mo and As and their movement through a wetland filter needs to be further investigated. It was concluded that molybdate is the least strongly held oxyanion and that a decrease in solution pH within the wetland would greatly improve Mo removal efficiency.

Effectiveness of implementation interventions in improving physician adherence to guideline recommendations in heart failure: a systematic review.

BACKGROUND: The uptake of guideline recommendations that improve heart failure (HF) outcomes remains suboptimal. We reviewed implementation interventions that improve physician adherence to these recommendations, and identified contextual factors associated with implementation success. METHODS: We searched databases from January 1990 to November 2017 for studies testing interventions to improve uptake of class I HF guidelines. We used the Cochrane Effective Practice and Organisation of Care and Process Redesign frameworks for data extraction. Primary outcomes included: proportion of eligible patients offered guideline-recommended pharmacotherapy, self-care education, left ventricular function assessment and/or intracardiac devices. We reported clinical outcomes when available. RESULTS: We included 38 studies. Provider-level interventions (n=13 studies) included audit and feedback, reminders and education. Organisation-level interventions (n=18) included medical records system changes, multidisciplinary teams, clinical pathways and continuity of care. System-level interventions (n=3) included provider/institutional incentives. Four studies assessed multi-level interventions. We could not perform meta-analyses due to statistical/conceptual heterogeneity. Thirty-two studies reported significant improvements in at least one primary outcome. Clinical pathways, multidisciplinary teams and multifaceted interventions were most consistently successful in increasing physician uptake of guidelines. Among randomised controlled trials (RCT) (n=10), pharmacist and nurse-led interventions improved target dose prescriptions. Eleven studies reported clinical outcomes; significant improvements were reported in three, including a clinical pathway, a multidisciplinary team and a multifaceted intervention. Baseline assessment of barriers, staff training, iterative intervention development, leadership commitment and policy/financial incentives were associated with intervention effectiveness. Most studies (n=20) had medium risk of bias; nine RCTs had low risk of bias. CONCLUSION: Our study is limited by the quality and heterogeneity of the primary studies. Clinical pathways, multidisciplinary teams and multifaceted interventions appear to be most consistent in increasing guideline uptake. However, improvements in process outcomes were rarely accompanied by improvements in clinical outcomes. Our work highlights the need for improved research methodology to reliably assess the effectiveness of implementation interventions.

Core Competencies in Evidence-Based Practice for Health Professionals: Consensus Statement Based on a Systematic Review and Delphi Survey.

Importance: Evidence-based practice (EBP) is necessary for improving the quality of health care as well as patient outcomes. Evidence-based practice is commonly integrated into the curricula of undergraduate, postgraduate, and continuing professional development health programs. There is, however, inconsistency in the curriculum content of EBP teaching and learning programs. A standardized set of minimum core competencies in EBP that health professionals should meet has the potential to standardize and improve education in EBP. Objective: To develop a consensus set of core competencies for health professionals in EBP. Evidence Review: For this modified Delphi survey study, a set of EBP core competencies that should be covered in EBP teaching and learning programs was developed in 4 stages: (1) generation of an initial set of relevant EBP competencies derived from a systematic review of EBP education studies for health professionals; (2) a 2-round, web-based Delphi survey of health professionals, selected using purposive sampling, to prioritize and gain consensus on the most essential EBP core competencies; (3) consensus meetings, both face-to-face and via video conference, to finalize the consensus on the most essential core competencies; and (4) feedback and endorsement from EBP experts. Findings: From an earlier systematic review of 83 EBP educational intervention studies, 86 unique EBP competencies were identified. In a Delphi survey of 234 participants representing a range of health professionals (physicians, nurses, and allied health professionals) who registered interest (88 [61.1%] women; mean [SD] age, 45.2 [10.2] years), 184 (78.6%) participated in round 1 and 144 (61.5%) in round 2. Consensus was reached on 68 EBP core competencies. The final set of EBP core competencies were grouped into the main EBP domains. For each key competency, a description of the level of detail or delivery was identified. Conclusions and Relevance: A consensus-based, contemporary set of EBP core competencies has been identified that may inform curriculum development of entry-level EBP teaching and learning programs for health professionals and benchmark standards for EBP teaching.