Enhancing the coverage of SemRep using a relation classification approach.

OBJECTIVE: Relation extraction is an essential task in the field of biomedical literature mining and offers significant benefits for various downstream applications, including database curation, drug repurposing, and literature-based discovery. The broad-coverage natural language processing (NLP) tool SemRep has established a solid baseline for extracting subject-predicate-object triples from biomedical text and has served as the backbone of the Semantic MEDLINE Database (SemMedDB), a PubMed-scale repository of semantic triples. While SemRep achieves reasonable precision (0.69), its recall is relatively low (0.42). In this study, we aimed to enhance SemRep using a relation classification approach, in order to eventually increase the size and the utility of SemMedDB. METHODS: We combined and extended existing SemRep evaluation datasets to generate training data. We leveraged the pre-trained PubMedBERT model, enhancing it through additional contrastive pre-training and fine-tuning. We experimented with three entity representations: mentions, semantic types, and semantic groups. We evaluated the model performance on a portion of the SemRep Gold Standard dataset and compared it to SemRep performance. We also assessed the effect of the model on a larger set of 12K randomly selected PubMed abstracts. RESULTS: Our results show that the best model yields a precision of 0.62, recall of 0.81, and F1 score of 0.70. Assessment on 12K abstracts shows that the model could double the size of SemMedDB, when applied to entire PubMed. We also manually assessed the quality of 506 triples predicted by the model that SemRep had not previously identified, and found that 67% of these triples were correct. CONCLUSION: These findings underscore the promise of our model in achieving a more comprehensive coverage of relationships mentioned in biomedical literature, thereby showing its potential in enhancing various downstream applications of biomedical literature mining. Data and code related to this study are available at https://github.com/Michelle-Mings/SemRep_RelationClassification.

Alpha-lipoic acid for diabetic peripheral neuropathy.

BACKGROUND: Diabetic peripheral neuropathy (DPN) is a frequent complication in people living with type 1 or type 2 diabetes. There is currently no effective treatment for DPN. Although alpha-lipoic acid (ALA, also known as thioctic acid) is widely used, there is no consensus about its benefits and harms. OBJECTIVES: To assess the effects of alpha-lipoic acid as a disease-modifying agent in people with diabetic peripheral neuropathy. SEARCH METHODS: On 11 September 2022, we searched the Cochrane Neuromuscular Specialised Register, CENTRAL, MEDLINE, Embase, and two clinical trials registers. We also searched the reference lists of the included studies and relevant review articles for additional references not identified by the electronic searches. SELECTION CRITERIA: We included randomised clinical trials (RCTs) that compared ALA with placebo in adults (aged 18 years or older) and that applied the study interventions for at least six months. There were no language restrictions. DATA COLLECTION AND ANALYSIS: We used standard methods expected by Cochrane. The primary outcome was change in neuropathy symptoms expressed as changes in the Total Symptom Score (TSS) at six months after randomisation. Secondary outcomes were change in neuropathy symptoms at six to 12 months and at 12 to 24 months, change in impairment, change in any validated quality of life total score, complications of DPN, and adverse events. We assessed the certainty of the evidence using GRADE. MAIN RESULTS: Our analysis incorporated three trials involving 816 participants. Two studies included people with type 1 or type 2 diabetes, while one study included only people with type 2 diabetes. The duration of treatment was between six months and 48 months. We judged all studies at high risk of overall bias due to attrition. ALA compared with placebo probably has little or no effect on neuropathy symptoms measured by TSS (lower score is better) after six months (mean difference (MD) -0.16 points, 95% confidence interval (CI) -0.83 to 0.51; 1 study, 330 participants; moderate-certainty evidence). The CI of this effect estimate did not contain the minimal clinically important difference (MCID) of 0.97 points. ALA compared with placebo may have little or no effect on impairment measured by the Neuropathy Impairment Score-Lower Limbs (NIS-LL; lower score is better) after six months (MD -1.02 points, 95% CI -2.93 to 0.89; 1 study, 245 participants; low-certainty evidence). However, we cannot rule out a significant benefit, because the lower limit of the CI surpassed the MCID of 2 points. There is probably little or no difference between ALA and placebo in terms of adverse events leading to cessation of treatment within six months (risk ratio (RR) 1.48, 95% CI 0.50 to 4.35; 3 studies, 1090 participants; moderate-certainty evidence). No studies reported quality of life or complications associated with DPN. AUTHORS' CONCLUSIONS: Our analysis suggests that ALA probably has little or no effect on neuropathy symptoms or adverse events at six months, and may have little or no effect on impairment at six months. All the studies were at high risk of attrition bias. Therefore, future RCTs should ensure complete follow-up and transparent reporting of any participants missing from the analyses.

Assessing the research landscape and clinical utility of large language models: a scoping review.

IMPORTANCE: Large language models (LLMs) like OpenAI's ChatGPT are powerful generative systems that rapidly synthesize natural language responses. Research on LLMs has revealed their potential and pitfalls, especially in clinical settings. However, the evolving landscape of LLM research in medicine has left several gaps regarding their evaluation, application, and evidence base. OBJECTIVE: This scoping review aims to (1) summarize current research evidence on the accuracy and efficacy of LLMs in medical applications, (2) discuss the ethical, legal, logistical, and socioeconomic implications of LLM use in clinical settings, (3) explore barriers and facilitators to LLM implementation in healthcare, (4) propose a standardized evaluation framework for assessing LLMs' clinical utility, and (5) identify evidence gaps and propose future research directions for LLMs in clinical applications. EVIDENCE REVIEW: We screened 4,036 records from MEDLINE, EMBASE, CINAHL, medRxiv, bioRxiv, and arXiv from January 2023 (inception of the search) to June 26, 2023 for English-language papers and analyzed findings from 55 worldwide studies. Quality of evidence was reported based on the Oxford Centre for Evidence-based Medicine recommendations. FINDINGS: Our results demonstrate that LLMs show promise in compiling patient notes, assisting patients in navigating the healthcare system, and to some extent, supporting clinical decision-making when combined with human oversight. However, their utilization is limited by biases in training data that may harm patients, the generation of inaccurate but convincing information, and ethical, legal, socioeconomic, and privacy concerns. We also identified a lack of standardized methods for evaluating LLMs' effectiveness and feasibility. CONCLUSIONS AND RELEVANCE: This review thus highlights potential future directions and questions to address these limitations and to further explore LLMs' potential in enhancing healthcare delivery.

Using word evolution to predict drug repurposing.

BACKGROUND: Traditional literature based discovery is based on connecting knowledge pairs extracted from separate publications via a common mid point to derive previously unseen knowledge pairs. To avoid the over generation often associated with this approach, we explore an alternative method based on word evolution. Word evolution examines the changing contexts of a word to identify changes in its meaning or associations. We investigate the possibility of using changing word contexts to detect drugs suitable for repurposing. RESULTS: Word embeddings, which represent a word's context, are constructed from chronologically ordered publications in MEDLINE at bi-monthly intervals, yielding a time series of word embeddings for each word. Focusing on clinical drugs only, any drugs repurposed in the final time segment of the time series are annotated as positive examples. The decision regarding the drug's repurposing is based either on the Unified Medical Language System (UMLS), or semantic triples extracted using SemRep from MEDLINE. CONCLUSIONS: The annotated data allows deep learning classification, with a 5-fold cross validation, to be performed and multiple architectures to be explored. Performance of 65% using UMLS labels, and 81% using SemRep labels is attained, indicating the technique's suitability for the detection of candidate drugs for repurposing. The investigation also shows that different architectures are linked to the quantities of training data available and therefore that different models should be trained for every annotation approach.

Probiotics as an effective therapeutic approach in alleviating depression symptoms: an umbrella meta-analysis.

Growing evidence has suggested that the consumption of probiotics can decrease depressive symptoms. However, even the results of meta-analyses are conflicting. In this regard, we performed an umbrella meta-analysis and proposed the decisive impacts of probiotics on depressive symptoms. The following international databases were searched up to July 2021: PubMed/Medline, Web of Science, Scopus, EMBASE, and Google Scholar. Meta-analyses investigating the impact of supplementation of probiotics on depression symptoms in adults were included. According to the studies, random-effects model was used to perform the analysis. Subgroup analysis was performed by dosage of probiotics, duration of supplementation and total sample size. Publication bias was assessed using Egger's, Begg's and visual inspection of funnel plot. Ten meta-analyses (n = 8886 participants) were included in study. The pooled data indicated that probiotic supplementation significantly reduced depression symptoms (ES= -1.41; 95% CI: -2.53, -0.30, p = 0.016; I2 = 99.4, p = <0.001). Subgroup analysis of studies with intervention duration >8 weeks and dosage >10 × 109 CFU demonstrated a more robust effect of probiotics on decreasing depression symptoms. There was also significant between-study heterogeneity in which dosage was identified as source of it. The results of present umbrella meta-analysis suggest administration of probiotics for relieving depression symptoms for >8 weeks with dosage of >10 × 109 CFU.Supplemental data for this article is available online at https://doi.org/10.1080/10408398.2022.2051164.

Coffee consumption and caffeine intake in relation to risk of fractures: a systematic review and dose-response meta-analysis of observational studies.

BACKGROUND: Conflicting reports are available about the association of coffee or caffeine intake and risk of fracture. We performed the current updated systematic review and dose-response meta-analysis of coffee consumption and caffeine intake and risk of fracture to quantify this association. MATERIALS AND METHODS: PubMed/Medline, ISI Web of Science, and Scopus, Cochrane database were searched up to July 2021. Random-effects model or fixed-effects model was used to pool the study-specific effect sizes (ESs) and 95% confidence intervals (CIs). Dose-response relationship was examined using linear and non-linear dose-response analyses. The certainty of evidence was assessed using NutriGrade tool. RESULTS: Out of 22 eligible studies included in the meta-analysis, 15 had cohort and 7 had case-control design. We found no significant association between coffee consumption and risk of fracture, either based on pooling cohort (RR: 0.99; 95% CI: 0.88, 1.12; I2 = 71.4%, Pheterogeneity < 0.01) or case-control studies (OR: 1.13; 95% CI: 0.87, 1.46; I2 = 49.0%, Pheterogeneity=0.08). In the subgroup analysis of cohort studies, we observed that higher coffee intake was inversely associated with risk of fracture in men (RR: 0.85; 95% CI: 0.76 to 0.94). In addition, a positive association was seen between coffee consumption and risk of fracture in studies with less than 12 years of follow-up (RR: 1.14; 95% CI: 1.02 to 1.27). With regard to caffeine intake, a statistically significant positive association was seen with risk of fracture (RR: 1.15; 95% CI, 1.08 to 1.23; I2=26.6%, n = 8). In the dose-response analysis, we found that each additional 100 mg caffeine intake was marginally associated with 2% greater risk of fracture (RR: 1.02; 95% CI: 1 to 1.05; I2= 70.3%, n = 6). CONCLUSION: High coffee consumption was protectively associated with risk of fracture in men, while caffeine intake was positive associated with risk.

Systematic Review and Meta-Analysis of the Pooled Rate of Post-Thrombotic Syndrome After Isolated Distal Deep Venous Thrombosis.

OBJECTIVE: To identify the rate of post-thrombotic syndrome (PTS) after isolated distal deep venous thrombosis (IDDVT) by performing a meta-analysis of the rate of PTS across randomised and observational studies. DATA SOURCES: MEDLINE, Embase, the Cochrane Controlled Trials Register, Clinicaltrials.gov, European Union Clinical Trials, International Standard Randomised Controlled Trial Number, and the Australian and New-Zealand Trials Registries. REVIEW METHODS: This review followed PRISMA guidelines using a registered protocol (CRD42021282136). Databases were searched up to December 2021 and prospective studies reporting the development of post-thrombotic syndrome were included; these were pooled with the meta-analysis. RESULTS: The results showed a post-thrombotic rate of 17% (95% CI 11 - 26%) (seven studies, 217 cases, 1 105 participants). Heterogeneity was high (I2 = 89%). On meta-regression, the rate of post-thrombotic syndrome was not correlated with the length of follow up (p = .71). Three studies (302 participants) reported the severity of post-thrombotic syndrome: 78% were mild (Villalta score 5 - 9); 11% were moderate (Villalta score 10 - 14), and 11% were severe (Villalta score ≥ 15). CONCLUSION: The risk of post-thrombotic syndrome after IDDVT was one in five and the risk of severe clinical manifestations, including ulceration, was one in 50. There was significant clinical, methodological, and statistical heterogeneity between studies and a substantial risk of bias from pooled studies. Randomised trials to support interventions for prevention of post-thrombotic syndrome are urgently needed.

Implementation of the trial emulation approach in medical research: a scoping review.

BACKGROUND: When conducting randomised controlled trials is impractical, an alternative is to carry out an observational study. However, making valid causal inferences from observational data is challenging because of the risk of several statistical biases. In 2016 Hernán and Robins put forward the 'target trial framework' as a guide to best design and analyse observational studies whilst preventing the most common biases. This framework consists of (1) clearly defining a causal question about an intervention, (2) specifying the protocol of the hypothetical trial, and (3) explaining how the observational data will be used to emulate it. METHODS: The aim of this scoping review was to identify and review all explicit attempts of trial emulation studies across all medical fields. Embase, Medline and Web of Science were searched for trial emulation studies published in English from database inception to February 25, 2021. The following information was extracted from studies that were deemed eligible for review: the subject area, the type of observational data that they leveraged, and the statistical methods they used to address the following biases: (A) confounding bias, (B) immortal time bias, and (C) selection bias. RESULTS: The search resulted in 617 studies, 38 of which we deemed eligible for review. Of those 38 studies, most focused on cardiology, infectious diseases or oncology and the majority used electronic health records/electronic medical records data and cohort studies data. Different statistical methods were used to address confounding at baseline and selection bias, predominantly conditioning on the confounders (N = 18/49, 37%) and inverse probability of censoring weighting (N = 7/20, 35%) respectively. Different approaches were used to address immortal time bias, assigning individuals to treatment strategies at start of follow-up based on their data available at that specific time (N = 21, 55%), using the sequential trial emulations approach (N = 11, 29%) or the cloning approach (N = 6, 16%). CONCLUSION: Different methods can be leveraged to address (A) confounding bias, (B) immortal time bias, and (C) selection bias. When working with observational data, and if possible, the 'target trial' framework should be used as it provides a structured conceptual approach to observational research.

Methodological issues of the electronic health records' use in the context of epidemiological investigations, in light of missing data: a review of the recent literature.

BACKGROUND: Electronic health records (EHRs) are widely accepted to enhance the health care quality, patient monitoring, and early prevention of various diseases, even when there is incomplete or missing information in them. AIM: The present review sought to investigate the impact of EHR implementation on healthcare quality and medical decision in the context of epidemiological investigations, considering missing or incomplete data. METHODS: Google scholar, Medline (via PubMed) and Scopus databases were searched for studies investigating the impact of EHR implementation on healthcare quality and medical decision, as well as for studies investigating the way of dealing with missing data, and their impact on medical decision and the development process of prediction models. Electronic searches were carried out up to 2022. RESULTS: EHRs were shown that they constitute an increasingly important tool for both physicians, decision makers and patients, which can improve national healthcare systems both for the convenience of patients and doctors, while they improve the quality of health care as well as they can also be used in order to save money. As far as the missing data handling techniques is concerned, several investigators have already tried to propose the best possible methodology, yet there is no wide consensus and acceptance in the scientific community, while there are also crucial gaps which should be addressed. CONCLUSIONS: Through the present thorough investigation, the importance of the EHRs' implementation in clinical practice was established, while at the same time the gap of knowledge regarding the missing data handling techniques was also pointed out.

Evaluation of reporting quality of cohort studies using real-world data based on RECORD: systematic review.

OBJECTIVE: Real-world data (RWD) and real-world evidence (RWE) have been paid more and more attention in recent years. We aimed to evaluate the reporting quality of cohort studies using real-world data (RWD) published between 2013 and 2021 and analyze the possible factors. METHODS: We conducted a comprehensive search in Medline and Embase through the OVID interface for cohort studies published from 2013 to 2021 on April 29, 2022. Studies aimed at comparing the effectiveness or safety of exposure factors in the real-world setting were included. The evaluation was based on the REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. Agreement for inclusion and evaluation was calculated using Cohen's kappa. Pearson chi-square test or Fisher's exact test and Mann-Whitney U test were used to analyze the possible factors, including the release of RECORD, journal IFs, and article citations. Bonferroni's correction was conducted for multiple comparisons. Interrupted time series analysis was performed to display the changes in report quality over time. RESULTS: 187 articles were finally included. The mean ± SD of the percentage of adequately reported items in the 187 articles was 44.7 ± 14.3 with a range of 11.1-87%. Of 23 items, the adequate reporting rate of 10 items reached 50%, and the reporting rate of some vital items was inadequate. After Bonferroni's correction, the reporting of only one item significantly improved after the release of RECORD and there was no significant improvement in the overall report quality. For interrupted time series analysis, there were no significant changes in the slope (p = 0.42) and level (p = 0.12) of adequate reporting rate. The journal IFs and citations were respectively related to 2 areas and the former significantly higher in high-reporting quality articles. CONCLUSION: The endorsement of the RECORD cheklist was generally inadequate in cohort studies using RWD and has not improved in recent years. We encourage researchers to endorse relevant guidelines when utilizing RWD for research.

Few-shot learning for medical text: A review of advances, trends, and opportunities.

BACKGROUND: Few-shot learning (FSL) is a class of machine learning methods that require small numbers of labeled instances for training. With many medical topics having limited annotated text-based data in practical settings, FSL-based natural language processing (NLP) holds substantial promise. We aimed to conduct a review to explore the current state of FSL methods for medical NLP. METHODS: We searched for articles published between January 2016 and October 2022 using PubMed/Medline, Embase, ACL Anthology, and IEEE Xplore Digital Library. We also searched the preprint servers (e.g., arXiv, medRxiv, and bioRxiv) via Google Scholar to identify the latest relevant methods. We included all articles that involved FSL and any form of medical text. We abstracted articles based on the data source, target task, training set size, primary method(s)/approach(es), and evaluation metric(s). RESULTS: Fifty-one articles met our inclusion criteria-all published after 2018, and most since 2020 (42/51; 82%). Concept extraction/named entity recognition was the most frequently addressed task (21/51; 41%), followed by text classification (16/51; 31%). Thirty-two (61%) articles reconstructed existing datasets to fit few-shot scenarios, and MIMIC-III was the most frequently used dataset (10/51; 20%). 77% of the articles attempted to incorporate prior knowledge to augment the small datasets available for training. Common methods included FSL with attention mechanisms (20/51; 39%), prototypical networks (11/51; 22%), meta-learning (7/51; 14%), and prompt-based learning methods, the latter being particularly popular since 2021. Benchmarking experiments demonstrated relative underperformance of FSL methods on biomedical NLP tasks. CONCLUSION: Despite the potential for FSL in biomedical NLP, progress has been limited. This may be attributed to the rarity of specialized data, lack of standardized evaluation criteria, and the underperformance of FSL methods on biomedical topics. The creation of publicly-available specialized datasets for biomedical FSL may aid method development by facilitating comparative analyses.

Search strategies (filters) to identify systematic reviews in MEDLINE and Embase.

BACKGROUND: Bibliographic databases provide access to an international body of scientific literature in health and medical sciences. Systematic reviews are an important source of evidence for clinicians, researchers, consumers, and policymakers as they address a specific health-related question and use explicit methods to identify, appraise and synthesize evidence from which conclusions can be drawn and decisions made. Methodological search filters help database end-users search the literature effectively with different levels of sensitivity and specificity. These filters have been developed for various study designs and have been found to be particularly useful for intervention studies. Other filters have been developed for finding systematic reviews. Considering the variety and number of available search filters for systematic reviews, there is a need for a review of them in order to provide evidence about their retrieval properties at the time they were developed. OBJECTIVES: To review systematically empirical studies that report the development, evaluation, or comparison of search filters to retrieve reports of systematic reviews in MEDLINE and Embase. SEARCH METHODS: We searched the following databases from inception to January 2023: MEDLINE, Embase, PsycINFO; Library, Information Science & Technology Abstracts (LISTA) and Science Citation Index (Web of Science). SELECTION CRITERIA: We included studies if one of their primary objectives is the development, evaluation, or comparison of a search filter that could be used to retrieve systematic reviews on MEDLINE, Embase, or both. DATA COLLECTION AND ANALYSIS: Two review authors independently extracted data using a pre-specified and piloted data extraction form using InterTASC Information Specialist Subgroup (ISSG) Search Filter Evaluation Checklist. MAIN RESULTS: We identified eight studies that developed filters for MEDLINE and three studies that developed filters for Embase. Most studies are very old and some were limited to systematic reviews in specific clinical areas. Six included studies reported the sensitivity of their developed filter. Seven studies reported precision and six studies reported specificity. Only one study reported the number needed to read and positive predictive value. None of the filters were designed to differentiate systematic reviews on the basis of their methodological quality. For MEDLINE, all filters showed similar sensitivity and precision, and one filter showed higher levels of specificity. For Embase, filters showed variable sensitivity and precision, with limited study reports that may affect accuracy assessments. The report of these studies had some limitations, and the assessments of their accuracy may suffer from indirectness, considering that they were mostly developed before the release of the PRISMA 2009 statement or due to their limited scope in the selection of systematic review topics. Search filters for MEDLINE Three studies produced filters with sensitivity > 90% with variable degrees of precision, and only one of them was developed and validated in a gold-standard database, which allowed the calculation of specificity. The other two search filters had lower levels of sensitivity. One of these produced a filter with higher levels of specificity (> 90%). All filters showed similar sensitivity and precision in the external validation, except for one which was not externally validated and another one which was conceptually derived and only externally validated. Search filters for Embase We identified three studies that developed filters for this database. One of these studies developed filters with variable sensitivity and precision, including highly sensitive strategies (> 90%); however, it was not externally validated. The other study produced a filter with a lower sensitivity (72.7%) but high specificity (99.1%) with a similar performance in the external validation. AUTHORS' CONCLUSIONS: Studies reporting the development, evaluation, or comparison of search filters to retrieve reports of systematic reviews in MEDLINE showed similar sensitivity and precision, with one filter showing higher levels of specificity. For Embase, filters showed variable sensitivity and precision, with limited information about how the filter was produced, which leaves us uncertain about their performance assessments. Newer filters had limitations in their methods or scope, including very focused subject topics for their gold standards, limiting their applicability across other topics. Our findings highlight that consensus guidance on the conduct of search filters and standardized reporting of search filters are needed, as we found highly heterogeneous development methods, accuracy assessments and outcome selection. New strategies adaptable across interfaces could enhance their usability. Moreover, the performance of existing filters needs to be evaluated in light of the impact of reporting guidelines, including the PRISMA 2009, on how systematic reviews are reported. Finally, future filter developments should also consider comparing the filters against a common reference set to establish comparative performance and assess the quality of systematic reviews retrieved by strategies.

Acute assessment services for patient flow assistance in hospital emergency departments.

BACKGROUND: Emergency departments (EDs) are facing serious and significant issues in the delivery of effective and efficient care to patients. Acute assessment services have been implemented at many hospitals internationally to assist in maintaining patient flow for identified groups of patients attending the ED. Identifying the risks and benefits, and optimal configurations of these services may be beneficial to those wishing to utilise an acute assessment service to improve patient flow. OBJECTIVES: To assess the effects of acute assessment services on patient flow following attendance at a hospital ED. SEARCH METHODS: We searched MEDLINE, CENTRAL, Embase and two trials registers on 24 September 2022 to identify studies. No restrictions were imposed on publication year, publication type, or publication language. SELECTION CRITERIA: Studies eligible for inclusion were randomised trials and cluster-randomised trials with at least two intervention and two control sites. Participants were adults (as defined by study authors) receiving care either in the ED or the acute assessment service, where both were based in the hospital setting. The comparison was hospital-based acute assessment services with usual, ED-only care. The outcomes of this review were mortality at time point closest to 30 days, length of stay in the service (in minutes), and waiting time to see a doctor (in minutes). DATA COLLECTION AND ANALYSIS: We followed the standard procedures of Cochrane Effective Practice and Organisation of Care for this review (https://epoc.cochrane.org/resources). MAIN RESULTS: We identified a total of 5754 records in the search. Following assessment of 3609 de-duplicated records, none were found to be eligible for inclusion in this review. AUTHORS' CONCLUSIONS: At present there are no randomised controlled trials exploring the effects of acute assessment services on patient flow in hospital-based emergency departments compared to usual, ED-only care.

Precision and Recall of Search Strategies for Identifying Studies on Work-Related Psychosocial Risk Factors in PubMed.

PURPOSE: This study aims to report on the effectiveness of various search strategies and keywords to find studies on work-related psychosocial risk factors (PRF) in the PubMed bibliographic database. METHODS: We first selected by hand-searching 191articles published on PRF and indexed in PubMed. We extracted 30 relevant MeSH terms and 38 additional textwords. We then searched PubMed combining these 68 keywords and 27 general keywords on work-related factors. Among the 2953 articles published in January 2020, we identified 446 articles concerning exposure to PRF, which were gathered in a Gold Standard database. We then computed the Recall, Precision, and Number Needed to Read of each keyword or combination of keywords. RESULTS: Overall, 189 search-words alone or in combination were tested. The highest Recall with a single MeSH term or textword was 43% and 35%, respectively. Subsequently, we developed two different search strings, one optimizing Recall while keeping Precision acceptable (Recall 98.2%, Precision 5.9%, NNR 16.9) and one optimizing Precision while keeping Recall acceptable (Recall 73.1%, Precision 25.5%, NNR 9.7). CONCLUSIONS: No single MeSH term is available to identify relevant studies on PRF in PubMed. Locating these types of studies requires the use of various MeSH and non-MeSH terms in combination to obtain a satisfactory Recall. Nevertheless, enhancing the Recall of search strategies may lead to lower Precision, and higher NNR, although with a non-linear trend. This factor must be taken into consideration when searching PubMed.

Journals accepting case reports.

Background: Few resources exist to support finding journals that accept case reports by specialty. In 2016, Katherine Akers compiled a list of 160 journals that accepted case reports, which many librarians continue to use 7 years later. Because journals' editorial policies and submission guidelines evolve, finding publication venues for case reports poses a dynamic problem, consisting of reviewing a journal's author guidelines to determine if the journal accepts case report manuscripts. This project aimed to create a more up to date and extensive list of journals that currently accept case reports. Case Presentation: 1,874 journal titles were downloaded from PubMed. The team reviewed each journal and identified journal titles that accept case reports. Additional inclusion factors included being indexed in MEDLINE, accessible on the internet, and accepting and publishing English language submissions. Discussion: The new journal list includes 1,028 journals covering 129 specialties and is available on the Open Science Framework public page.

Sub-Saharan Africa's biomedical journal coverage in scholarly databases: a comparison of Web of Science, Scopus, EMBASE, MEDLINE, African Index Medicus, and African Journals Online.

Objective: This study aims to find out the coverage of biomedical journals published in Sub-Saharan Africa in four authoritative international databases-Web of Science, Scopus, MEDLINE and EMBASE and two Africa-focused scholarly databases-Africa Journals Online (AJOL) and African Index Medicus (AIM). Methods: Lists of active journals that are published in the 46 Sub-Saharan African countries were retrieved from the Ulrich periodical directory to create master journal lists. Unique journals from other databases that were not found in Ulrich were added to the master journal list. The six databases included in this study were searched for journals on the master lists. Results: Only 23 of the 46 Sub-Saharan African countries had at least one biomedical journal. Only about one-quarter (152) of the 560 biomedical journals from Sub-Saharan Africa were found in at least one of the biomedical databases. South African journals accounted for more than 50% of all the Sub-Saharan journals in the international scholarly databases. AJOL contains the highest number of biomedical journals from Sub-Saharan Africa, followed by Scopus and EMBASE. AJOL asserts its importance by covering the highest number of unique journals and having a representative number of journals in all biomedical sub-disciplines. Conclusion: The majority of studies from Sub-Saharan Africa are left out when biomedical evidence-based researchers only retrieve studies from authoritative international databases. Searching Google Scholar and the African research databases of AJOL and AIM would increase the number of studies from the region.

Optimizing the literature search: coverage of included references in systematic reviews in Medline and Embase.

Objective: The aim of this study was to investigate if the included references in a set of completed systematic reviews are indexed in Ovid MEDLINE and Ovid Embase, and how many references would be missed if we were to constrict our literature searches to one of these sources, or the two databases in combination. Methods: We conducted a cross-sectional study where we searched for each included reference (n = 4,709) in 274 reviews produced by the Norwegian Institute of Public Health to find out if the references were indexed in the respective databases. The data was recorded in an Excel spreadsheet where we calculated the indexing rate. The reviews were sorted into eight categories to see if the indexing rate differs from subject to subject. Results: The indexing rate in MEDLINE (86.6%) was slightly lower than in Embase (88.2%). Without the MEDLINE records in Embase, the indexing rate in Embase was 71.8%. The highest indexing rate was achieved by combining both databases (90.2%). The indexing rate was highest in the category "Physical health - treatment" (97.4%). The category "Welfare" had the lowest indexing rate (58.9%). Conclusion: Our data reveals that 9.8% of the references are not indexed in either database. Furthermore, in 5% of the reviews, the indexing rate was 50% or lower.

Automated indexing using NLM's Medical Text Indexer (MTI) compared to human indexing in Medline: a pilot study.

Objective: In 2002, the National Library of Medicine (NLM) introduced semi-automated indexing of Medline using the Medical Text Indexer (MTI). In 2021, NLM announced that it would fully automate its indexing in Medline with an improved MTI by mid-2022. This pilot study examines indexing using a sample of records in Medline from 2000, and how an early, public version of MTI's outputs compares to records created by human indexers. Methods: This pilot study examines twenty Medline records from 2000, a year before the MTI was introduced as a MeSH term recommender. We identified twenty higher- and lower-impact biomedical journals based on Journal Impact Factor (JIF) and examined the indexing of papers by feeding their PubMed records into the Interactive MTI tool. Results: In the sample, we found key differences between automated and human-indexed Medline records: MTI assigned more terms and used them more accurately for citations in the higher JIF group, and MTI tended to rank the Male check tag more highly than the Female check tag and to omit Aged check tags. Sometimes MTI chose more specific terms than human indexers but was inconsistent in applying specificity principles. Conclusion: NLM's transition to fully automated indexing of the biomedical literature could introduce or perpetuate inconsistencies and biases in Medline. Librarians and searchers should assess changes to index terms, and their impact on PubMed's mapping features for a range of topics. Future research should evaluate automated indexing as it pertains to finding clinical information effectively, and in performing systematic searches.

Application of text mining to the development and validation of a geographic search filter to facilitate evidence retrieval in Ovid MEDLINE: An example from the United States.

BACKGROUND: Given the increasing volume of published research in bibliographic databases, efficient retrieval of evidence is crucial and represents an opportunity to integrate novel techniques such as text mining. OBJECTIVES: To develop and validate a geographic search filter for identifying research from the United States (US) in Ovid MEDLINE. METHODS: US and non-US citations were collected from bibliographies of evidence-based reviews. Citations were partitioned by US/non-US status and randomly divided to a training and testing set. Using text mining, common one- and two-word terms in title/abstract fields were identified, and frequencies compared between US/non-US citations. RESULTS: Common US-related terms included (as ratio of frequency in US/non-US citations) US populations and geographic terms [e.g., 'Americans' (15.5), 'Baltimore' (20.0)]. Common non-US terms were non-US geographic terms [e.g., 'Japan' (0.04), 'French' (0.05)]. A search filter was developed with 98.3% sensitivity and 82.7% specificity. DISCUSSION: This search filter will streamline the identification of evidence from the US. Periodic updates may be necessary to reflect changes in MEDLINE's controlled vocabulary. CONCLUSION: Text mining was instrumental to the development of this search filter. A novel technique generated a gold standard set comprising >20,000 citations. This method may be adapted to develop subsequent geographic search filters.

Updated generic search filters for finding studies of adverse drug effects in Ovid medline and Embase may retrieve up to 90% of relevant studies.

BACKGROUND: The most current objectively derived search filters for adverse drug effects are 15 years old and other strategies have not been developed and tested empirically. OBJECTIVE: To develop and validate search filters to retrieve evidence on adverse drug effects from Ovid medline and Ovid Embase. METHODS: We identified systematic reviews of adverse drug effects in Epistemonikos. From these reviews, we collated their included studies which we then randomly divided into three tests and one validation set of records. We constructed a search strategy to maximise relative recall using word frequency analysis with test set one. This search strategy was then refined using test sets two and three and validated on the final set of records. RESULTS: Of 107 systematic reviews which met our inclusion criteria, 1948 unique included studies were available from medline and 1980 from Embase. Generic adverse drug effects searches in medline and Embase achieved 90% and 89% relative recall, respectively. When specific adverse effects terms were added recall was improved. CONCLUSION: We have derived and validated search filters that retrieve around 90% of records with adverse drug effects data in medline and Embase. The addition of specific adverse effects terms is required to achieve higher recall.