Mortality in people living with dementia who self-harmed: An Australian data linkage study.

OBJECTIVES: This study aimed to examine mortality for people living with dementia/mild cognitive impairment who self-harmed. METHODS: We conducted a retrospective cohort study in New South Wales, Australia, using data ranging from 2001 to 2015. From people who accessed hospital services in the study period, we identified 154,811 people living with dementia/mild cognitive impairment, 28,972 who self-harmed and 1511 who had a record of both dementia/mild cognitive impairment and self-harm. We examined rates, causes and predictors of death for people with dementia/mild cognitive impairment and/or self-harm diagnoses using flexible parametric survival analyses. We explored rates of repeat self-harm in people living with dementia who self-harmed. RESULTS: Circulatory disorders accounted for 32.0% of deaths in people with a living with dementia who self-harmed, followed by neoplasms (14.7%), and mental and behavioural disorders (9.6%). Death was more likely for someone who had self-harmed if they developed dementia/mild cognitive impairment. Predictors of death included male sex, greater physical comorbidity, a history of delirium, more previous emergency department presentations and fewer previous mental health ambulatory service days. Greater engagement with outpatient mental health services predicted a decreased likelihood of repeat self-harm. DISCUSSION: We found that mortality increases when people who self-harm develop dementia. We argue post-diagnosis support offers a potential opportunity to reduce mortality rates in people with both dementia and self-harm diagnoses.

Evaluating the risk of data loss due to particle radiation damage in a DNA data storage system.

DNA data storage is a potential alternative to magnetic tape for archival storage purposes, promising substantial gains in information density. Critical to the success of DNA as a storage media is an understanding of the role of environmental factors on the longevity of the stored information. In this paper, we evaluate the effect of exposure to ionizing particle radiation, a cause of data loss in traditional magnetic media, on the longevity of data in DNA data storage pools. We develop a mass action kinetics model to estimate the rate of damage accumulation in DNA strands due to neutron interactions with both nucleotides and residual water molecules, then utilize the model to evaluate the effect several design parameters of a typical DNA data storage scheme have on expected data longevity. Finally, we experimentally validate our model by exposing dried DNA samples to different levels of neutron irradiation and analyzing the resulting error profile. Our results show that particle radiation is not a significant contributor to data loss in DNA data storage pools under typical storage conditions.

Factors impacting hospitalisation and related health service costs in cancer survivors in Australia: Results from a population data linkage study in Queensland (COS-Q).

BACKGROUND: The global economic cost of cancer and the costs of ongoing care for survivors are increasing. Little is known about factors affecting hospitalisations and related costs for the growing number of cancer survivors. Our aim was to identify associated factors of cancer survivors admitted to hospital in the public system and their costs from a health services perspective. METHODS: A population-based, retrospective, data linkage study was conducted in Queensland (COS-Q), Australia, including individuals diagnosed with a first primary cancer who incurred healthcare costs between 2013 and 2016. Generalised linear models were fitted to explore associations between socio-demographic (age, sex, country of birth, marital status, occupation, geographic remoteness category and socio-economic index) and clinical (cancer type, year of/time since diagnosis, vital status and care type) factors with mean annual hospital costs and mean episode costs. RESULTS: Of the cohort (N = 230,380) 48.5% (n = 111,820) incurred hospitalisations in the public system (n = 682,483 admissions). Hospital costs were highest for individuals who died during the costing period (cost ratio 'CR': 1.79, p < 0.001) or living in very remote or remote location (CR: 1.71 and CR: 1.36, p < 0.001) or aged 0-24 years (CR: 1.63, p < 0.001). Episode costs were highest for individuals in rehabilitation or palliative care (CR: 2.94 and CR: 2.34, p < 0.001), or very remote location (CR: 2.10, p < 0.001). Higher contributors to overall hospital costs were 'diseases and disorders of the digestive system' (AU$661 m, 21% of admissions) and 'neoplastic disorders' (AU$554 m, 20% of admissions). CONCLUSIONS: We identified a range of factors associated with hospitalisation and higher hospital costs for cancer survivors, and our results clearly demonstrate very high public health costs of hospitalisation. There is a lack of obvious means to reduce these costs in the short or medium term which emphasises an increasing economic imperative to improving cancer prevention and investments in home- or community-based patient support services.

Data Policy Finder: an easily integratable tool connecting data librarians with researchers to navigate publication requirements.

The Data Policy Finder is a searchable database containing librarian-curated information, links, direct quotes from relevant policy sections, and notes to help the researcher search, verify, and plan for their publication data requirements. The Memorial Sloan Kettering Cancer Center Library launched this new resource to help researchers navigate the ever-growing, and widely varying body of publisher policies regarding data, code, and other supplemental materials. The project team designed this resource to encourage growth and collaboration with other librarians and information professionals facing similar challenges supporting their research communities. This resource creates another access point for researchers to connect with data management services and, as an open-source tool, it can be integrated into the workflows and support services of other libraries.

Data linkage studies of primary care utilisation after release from prison: a scoping review.

BACKGROUND: Primary care plays a central role in most, if not all, health care systems including the care of vulnerable populations such as people who have been incarcerated. Studies linking incarceration records to health care data can improve understanding about health care access following release from prison. This review maps evidence from data-linkage studies about primary care use after prison release. METHODS: The framework by Arksey and O'Malley and guidance by the Joanna Briggs Institute (JBI) were used in this review. This scoping review followed methods published in a study protocol. Searches were performed (January 2012-March 2023) in MEDLINE, EMBASE and Web of Science Core Collection using key-terms relating to two areas: (i) people who have been incarcerated and (ii) primary care. Using eligibility criteria, two authors independently screened publication titles and abstracts (step 1), and subsequently, screened full text publications (step 2). Discrepancies were resolved with a third author. Two authors independently charted data from included publications. Findings were mapped by methodology, key findings and gaps in research. RESULTS: The database searches generated 1,050 publications which were screened by title and abstract. Following this, publications were fully screened (n = 63 reviewer 1 and n = 87 reviewer 2), leading to the inclusion of 17 publications. Among the included studies, primary care use after prison release was variable. Early contact with primary care services after prison release (e.g. first month) was positively associated with an increased health service use, but an investigation found that a large proportion of individuals did not access primary care during the first month. The quality of care was found to be largely inadequate (measured continuity of care) for moderate multimorbidity. There were lower levels of colorectal and breast cancer screening among people released from custody. The review identified studies of enhanced primary care programmes for individuals following release from prison, with studies reporting a reduction in reincarceration and criminal justice system costs. CONCLUSIONS: This review has suggested mixed evidence regarding primary care use after prison release and has highlighted challenges and areas of suboptimal care. Further research has been discussed in relation to the scoping review findings.

Use of Beacon v2 for Improving Genomics Based Research in a Clinical Setting.

GA4GH has proposed the Beacon architecture as an interface to retrieve genomic information which also protects the privacy of the individuals. In this paper, we propose to adapt the Beacon Reference Implementation to the use case of a study comparing the susceptibility to the carcinogenic effects of tobacco. This analysis compares the germline of heavy smokers who have either never developed lung cancer or, on the contrary, have developed it at a young age. To adapt the Beacon Reference Implementation to the use case, we have added filtering capabilities and a new grouping of information allowing to retrieve the data by affected gene.

Exploring Offline Large Language Models for Clinical Information Extraction: A Study of Renal Histopathological Reports of Lupus Nephritis Patients.

Open source, lightweight and offline generative large language models (LLMs) hold promise for clinical information extraction due to their suitability to operate in secured environments using commodity hardware without token cost. By creating a simple lupus nephritis (LN) renal histopathology annotation schema and generating gold standard data, this study investigates prompt-based strategies using three state-of-the-art lightweight LLMs, namely BioMistral-DARE-7B (BioMistral), Llama-2-13B (Llama 2), and Mistral-7B-instruct-v0.2 (Mistral). We examine the performance of these LLMs within a zero-shot learning environment for renal histopathology report information extraction. Incorporating four prompting strategies, including combinations of batch prompt (BP), single task prompt (SP), chain of thought (CoT) and standard simple prompt (SSP), our findings indicate that both Mistral and BioMistral consistently demonstrated higher performance compared to Llama 2. Mistral recorded the highest performance, achieving an F1-score of 0.996 [95% CI: 0.993, 0.999] for extracting the numbers of various subtypes of glomeruli across all BP settings and 0.898 [95% CI: 0.871, 0.921] in extracting relational values of immune markers under the BP+SSP setting. This study underscores the capability of offline LLMs to provide accurate and secure clinical information extraction, which can serve as a promising alternative to their heavy-weight online counterparts.

Using Retrieval-Augmented Generation to Capture Molecularly-Driven Treatment Relationships for Precision Oncology.

Modern generative artificial intelligence techniques like retrieval-augmented generation (RAG) may be applied in support of precision oncology treatment discussions. Experts routinely review published literature for evidence and recommendations of treatments in a labor-intensive process. A RAG pipeline may help reduce this effort by providing chunks of text from these publications to an off-the-shelf large language model (LLM), allowing it to answer related questions without any fine-tuning. This potential application is demonstrated by retrieving treatment relationships from a trusted data source (OncoKB) and reproducing over 80% of them by asking simple questions to an untrained Llama 2 model with access to relevant abstracts.

Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning.

The increasing availability of biomedical data creates valuable resources for developing new deep learning algorithms to support experts, especially in domains where collecting large volumes of annotated data is not trivial. Biomedical data include several modalities containing complementary information, such as medical images and reports: images are often large and encode low-level information, while reports include a summarized high-level description of the findings identified within data and often only concerning a small part of the image. However, only a few methods allow to effectively link the visual content of images with the textual content of reports, preventing medical specialists from properly benefitting from the recent opportunities offered by deep learning models. This paper introduces a multimodal architecture creating a robust biomedical data representation encoding fine-grained text representations within image embeddings. The architecture aims to tackle data scarcity (combining supervised and self-supervised learning) and to create multimodal biomedical ontologies. The architecture is trained on over 6,000 colon whole slide Images (WSI), paired with the corresponding report, collected from two digital pathology workflows. The evaluation of the multimodal architecture involves three tasks: WSI classification (on data from pathology workflow and from public repositories), multimodal data retrieval, and linking between textual and visual concepts. Noticeably, the latter two tasks are available by architectural design without further training, showing that the multimodal architecture that can be adopted as a backbone to solve peculiar tasks. The multimodal data representation outperforms the unimodal one on the classification of colon WSIs and allows to halve the data needed to reach accurate performance, reducing the computational power required and thus the carbon footprint. The combination of images and reports exploiting self-supervised algorithms allows to mine databases without needing new annotations provided by experts, extracting new information. In particular, the multimodal visual ontology, linking semantic concepts to images, may pave the way to advancements in medicine and biomedical analysis domains, not limited to histopathology.

Towards Building an E-Librarian System: Exploring Recommendation System Preferences Among Ovarian Cancer Patients and Caregivers.

Ovarian cancer (OvCa) patients encounter complex treatment decisions, and often have difficulties in searching and integrating online health information to guide their treatment decision-making. The objective of this study was to explore the preference of online health information among OvCa patients and caregivers, by exploring their preferred content, format, and function features for the design of a personalized recommender system. This study used qualitative research methods to collect data through in-depth interviews with 18 OvCa patients and 2 caregivers. A total of (N=20) face-to-face interviews were conducted, and subsequently analyzed by audio recordings, verbatim transcription, and theory-driven approach with thematic analysis. A total of 5 themes were identified for content-related design, 4 themes identified for system function and one theme identified for frequency format. The results of this study inform the preference and therefore OvCa specific features can be tailor-made in a recommendation system.

First Australian estimates of incidence and prevalence of uterine fibroids: a data linkage cohort study 2000-2022.

STUDY QUESTION: What is the estimated prevalence and incidence of uterine fibroids diagnosed in Australian women of reproductive age? SUMMARY ANSWER: An estimated 7.3% of Australian women had a diagnosis of uterine fibroids by the age of 45-49 years, with age-specific incidence highest in women aged 40-44 years (5.0 cases per 1000 person-years). WHAT IS KNOWN ALREADY: Uterine fibroids are associated with a high symptom burden and may affect overall health and quality of life. Studies in different countries show a wide variation in both the prevalence (4.5-68%) and incidence (2.2-37.5 per 1000 person-years) of uterine fibroids, which may be partly explained by the type of investigation, method of case ascertainment, or the age range of the study population, necessitating the reporting of country-specific estimates. STUDY DESIGN, SIZE, DURATION: This observational prospective cohort study using self-report survey and linked administrative data (2000-2022) included 8066 women, born between 1973 and 1978, in the Australian Longitudinal Study on Women's Health. PARTICIPANTS/MATERIALS, SETTING, METHODS: A combination of self-report survey and linked administrative health data (hospital, emergency department, the Medicare Benefits Schedule, and the Pharmaceutical Benefits Scheme) were used to identify women with a report of a diagnosis of uterine fibroids between 2000 and 2022. MAIN RESULTS AND THE ROLE OF CHANCE: Of the 8066 Australian women followed for 22 years, an estimated 7.3% of women (95% CI 6.9, 7.6) had a diagnosis of uterine fibroids by the age of 45-49 years. The incidence increased with age and was highest in women aged 40-44 years (5.0 cases per 1000 person-years, 95% CI 4.3, 5.7 cases per 1000 person-years). Women with uterine fibroids were more likely to experience heavy or painful periods. They were also more likely to report low iron levels, endometriosis, and poor self-rated health and to have two or more annual visits to their general practitioner. LIMITATIONS, REASONS FOR CAUTION: Our estimates are based on self-report of doctor diagnosis or treatment for fibroids and/or data linked to treatment and procedure administrative records. This predominantly captures women with symptomatic fibroids, but has the potential for misclassification of asymptomatic women and an underestimate of overall prevalence and incidence. In addition, questions on fibroids were only asked in surveys when women were 37-42 years of age to 43-48 years of age, so cases at younger ages may have been underestimated (particularly in women with less severe symptoms) as these were only ascertained through data linkage. WIDER IMPLICATIONS OF THE FINDINGS: These are the first population-based estimates of the prevalence and incidence of uterine fibroids in women of reproductive age in Australia. Establishing these first estimates will help inform health policy and health care provision in the Australian context. STUDY FUNDING/COMPETING INTEREST(S): The ALSWH is funded by the Australian Government Department of Health and Aged Care. L.FW. was supported by an Australian National Health and Medical Research Council (NHMRC) Centres for Research Excellence grant (APP1153420) and G.D.M. was supported by an NHMRC Leadership Fellowship (APP2009577). The funding bodies played no role in the design, the collection, analysis or interpretation of data, the writing of the manuscript, or the decision to submit the manuscript for publication. There are no competing interests. TRIAL REGISTRATION NUMBER: N/A.

Development of a Longitudinal Prostate Cancer Transcriptomic and Clinical Data Linkage.

Importance: Although tissue-based gene expression testing has become widely used for prostate cancer risk stratification, its prognostic performance in the setting of clinical care is not well understood. Objective: To develop a linkage between a prostate genomic classifier (GC) and clinical data across payers and sites of care in the US. Design, Setting, and Participants: In this cohort study, clinical and transcriptomic data from clinical use of a prostate GC between 2016 and 2022 were linked with data aggregated from insurance claims, pharmacy records, and electronic health record (EHR) data. Participants were anonymously linked between datasets by deterministic methods through a deidentification engine using encrypted tokens. Algorithms were developed and refined for identifying prostate cancer diagnoses, treatment timing, and clinical outcomes using diagnosis codes, Common Procedural Terminology codes, pharmacy codes, Systematized Medical Nomenclature for Medicine clinical terms, and unstructured text in the EHR. Data analysis was performed from January 2023 to January 2024. Exposure: Diagnosis of prostate cancer. Main Outcomes and Measures: The primary outcomes were biochemical recurrence and development of prostate cancer metastases after diagnosis or radical prostatectomy (RP). The sensitivity of the linkage and identification algorithms for clinical and administrative data were calculated relative to clinical and pathological information obtained during the GC testing process as the reference standard. Results: A total of 92 976 of 95 578 (97.2%) participants who underwent prostate GC testing were successfully linked to administrative and clinical data, including 53 871 who underwent biopsy testing and 39 105 who underwent RP testing. The median (IQR) age at GC testing was 66.4 (61.0-71.0) years. The sensitivity of the EHR linkage data for prostate cancer diagnoses was 85.0% (95% CI, 84.7%-85.2%), including 80.8% (95% CI, 80.4%-81.1%) for biopsy-tested participants and 90.8% (95% CI, 90.5%-91.0%) for RP-tested participants. Year of treatment was concordant in 97.9% (95% CI, 97.7%-98.1%) of those undergoing GC testing at RP, and 86.0% (95% CI, 85.6%-86.4%) among participants undergoing biopsy testing. The sensitivity of the linkage was 48.6% (95% CI, 48.1%-49.1%) for identifying RP and 50.1% (95% CI, 49.7%-50.5%) for identifying prostate biopsy. Conclusions and Relevance: This study established a national-scale linkage of transcriptomic and longitudinal clinical data yielding high accuracy for identifying key clinical junctures, including diagnosis, treatment, and early cancer outcome. This resource can be leveraged to enhance understandings of disease biology, patterns of care, and treatment effectiveness.

Improving Clinical Registry Data Quality via Linkage With Survival Data From State-Based Population Registries.

PURPOSE: Real-world data (RWD) collected on patients treated as part of routine clinical care form the basis of cancer clinical registries. Capturing accurate death data can be challenging, with inaccurate survival data potentially compromising the integrity of registry-based research. Here, we explore the utility of data linkage (DL) to state-based registries to enhance the capture of survival outcomes. METHODS: We identified consecutive adult patients with brain tumors treated in the state of Victoria from the Brain Tumour Registry Australia: Innovation and Translation (BRAIN) database, who had no recorded date of death and no follow-up within the last 6 months. Full name and date of birth were used to match patients in the BRAIN registry with those in the Victorian Births, Deaths and Marriages (BDM) registry. Overall survival (OS) outcomes were compared pre- and post-DL. RESULTS: Of the 7,346 clinical registry patients, 5,462 (74%) had no date of death and no follow-up recorded within the last 6 months. Of the 5,462 patients, 1,588 (29%) were matched with a date of death in BDM. Factors associated with an increased number of matches were poor prognosis tumors, older age, and social disadvantage. OS was significantly overestimated pre-DL compared with post-DL for the entire cohort (pre- v post-DL: hazard ratio, 1.43; P < .001; median, 29.9 months v 16.7 months) and for most individual tumor types. This finding was present independent of the tumor prognosis. CONCLUSION: As revealed by linkage with BDM, a high proportion of patients in a brain cancer clinical registry had missing death data, contributed to by informative censoring, inflating OS calculations. DL to pertinent registries on an ongoing basis should be considered to ensure accurate reporting of survival data and interpretation of RWD outcomes.

ChatENT: Augmented Large Language Model for Expert Knowledge Retrieval in Otolaryngology-Head and Neck Surgery.

OBJECTIVE: The recent surge in popularity of large language models (LLMs), such as ChatGPT, has showcased their proficiency in medical examinations and potential applications in health care. However, LLMs possess inherent limitations, including inconsistent accuracy, specific prompting requirements, and the risk of generating harmful hallucinations. A domain-specific model might address these limitations effectively. STUDY DESIGN: Developmental design. SETTING: Virtual. METHODS: Otolaryngology-head and neck surgery (OHNS) relevant data were systematically gathered from open-access Internet sources and indexed into a knowledge database. We leveraged Retrieval-Augmented Language Modeling to recall this information and utilized it for pretraining, which was then integrated into ChatGPT4.0, creating an OHNS-specific knowledge question & answer platform known as ChatENT. The model is further tested on different types of questions. RESULTS: ChatENT showed enhanced performance in the analysis and interpretation of OHNS information, outperforming ChatGPT4.0 in both the Canadian Royal College OHNS sample examination questions challenge and the US board practice questions challenge, with a 58.4% and 26.0% error reduction, respectively. ChatENT generated fewer hallucinations and demonstrated greater consistency. CONCLUSION: To the best of our knowledge, ChatENT is the first specialty-specific knowledge retrieval artificial intelligence in the medical field that utilizes the latest LLM. It appears to have considerable promise in areas such as medical education, patient education, and clinical decision support. The model has demonstrated the capacity to overcome the limitations of existing LLMs, thereby signaling a future of more precise, safe, and user-friendly applications in the realm of OHNS and other medical fields.

RefAI: a GPT-powered retrieval-augmented generative tool for biomedical literature recommendation and summarization.

OBJECTIVES: Precise literature recommendation and summarization are crucial for biomedical professionals. While the latest iteration of generative pretrained transformer (GPT) incorporates 2 distinct modes-real-time search and pretrained model utilization-it encounters challenges in dealing with these tasks. Specifically, the real-time search can pinpoint some relevant articles but occasionally provides fabricated papers, whereas the pretrained model excels in generating well-structured summaries but struggles to cite specific sources. In response, this study introduces RefAI, an innovative retrieval-augmented generative tool designed to synergize the strengths of large language models (LLMs) while overcoming their limitations. MATERIALS AND METHODS: RefAI utilized PubMed for systematic literature retrieval, employed a novel multivariable algorithm for article recommendation, and leveraged GPT-4 turbo for summarization. Ten queries under 2 prevalent topics ("cancer immunotherapy and target therapy" and "LLMs in medicine") were chosen as use cases and 3 established counterparts (ChatGPT-4, ScholarAI, and Gemini) as our baselines. The evaluation was conducted by 10 domain experts through standard statistical analyses for performance comparison. RESULTS: The overall performance of RefAI surpassed that of the baselines across 5 evaluated dimensions-relevance and quality for literature recommendation, accuracy, comprehensiveness, and reference integration for summarization, with the majority exhibiting statistically significant improvements (P-values <.05). DISCUSSION: RefAI demonstrated substantial improvements in literature recommendation and summarization over existing tools, addressing issues like fabricated papers, metadata inaccuracies, restricted recommendations, and poor reference integration. CONCLUSION: By augmenting LLM with external resources and a novel ranking algorithm, RefAI is uniquely capable of recommending high-quality literature and generating well-structured summaries, holding the potential to meet the critical needs of biomedical professionals in navigating and synthesizing vast amounts of scientific literature.

Exploring the Feasibility of GPT-4 as a Data Extraction Tool for Renal Surgery Operative Notes.

INTRODUCTION: GPT-4 is a large language model with potential for multiple applications in urology. Our study sought to evaluate GPT-4's performance in data extraction from renal surgery operative notes. METHODS: GPT-4 was queried to extract information on laterality, surgery, approach, estimated blood loss, and ischemia time from deidentified operative notes. Match rates were determined by the number of "matched" data points between GPT-4 and human-curated extraction. Accuracy rates were calculated after manually reviewing "not matched" data points. Cohen's kappa and the intraclass coefficient were used to evaluate interrater agreement/reliability. RESULTS: Our cohort consisted of 1498 renal surgeries from 2003 to 2023. Match rates were high for laterality (94.4%), surgery (92.5%), and approach (89.4%), but lower for estimated blood loss (77.1%) and ischemia time (25.6%). GPT-4 was more accurate for estimated blood loss (90.3% vs 85.5% human curated) and similarly accurate for laterality (95.2% vs 95.3% human curated). Human-curated accuracy rates were higher for surgery (99.3% vs 93% GPT-4), approach (97.9% vs 90.8% GPT-4), and ischemia time (95.6% vs 30.7% GPT-4). Cohen's kappa was 0.96 for laterality, 0.83 for approach, and 0.71 for surgery. The intraclass coefficient was 0.62 for estimated blood loss and 0.09 for ischemia time. CONCLUSIONS: Match and accuracy rates were higher for categorical variables. GPT-4 data extraction was particularly error prone for variables with heterogenous documentation styles. The role of a standard operative template to aid data extraction will be explored in the future. GPT-4 can be utilized as a helpful and efficient data extraction tool with manual feedback.

Efficiency and comparability of using new evidence platforms for updating recommendations: experience with a type-2 diabetes guideline in Colombia / Eficiencia y comparabilidad del uso de nuevas plataformas de evidencia para la actualización de recomendaciones: experiencia con una guía de diabetes tipo 2 en Colombia

INTRODUCTION: Updating recommendations for guidelines requires a comprehensive and efficient literature search. Although new information platforms are available for developing groups, their relative contributions to this purpose remain uncertain. METHODS: As part of a review/update of eight selected evidence-based recommendationsfor type 2 diabetes, we evaluated the following five literature search approaches (targeting systematic reviews, using predetermined criteria): PubMed for MEDLINE, Epistemonikos database basic search, Epistemonikos database using a structured search strategy, Living overview of evidence (L.OVE) platform, and TRIP database. Three reviewers independently classified the retrieved references as definitely eligible, probably eligible, or not eligible. Those falling in the same "definitely" categories for all reviewers were labelled as "true" positives/negatives. The rest went to re-assessment and if found eligible/not eligible by consensus became "false" negatives/positives, respectively. We described the yield for each approach and computed "diagnostic accuracy" measures and agreement statistics. RESULTS: Altogether, the five approaches identified 318 to 505 references for the eight recommendations, from which reviewers considered 4.2 to 9.4% eligible after the two rounds. While Pubmed outperformed the other approaches (diagnostic odds ratio 12.5 versus 2.6 to 5.3), no single search approach returned eligible references for all recommendations. Individually, searches found up to 40% of all eligible references (n = 71), and no combination of any three approaches could find over 80% of them. Kappa statistics for retrieval between searches were very poor (9 out of 10 paired comparisons did not surpass the chance-expected agreement). CONCLUSION: Among the information platforms assessed, PubMed appeared to be more efficient in updating this set of recommendations. However, the very poor agreement among search approaches in the reference yield demands that developing groups add information from several (probably more than three) sources for this purpose. Further research is needed to replicate our findings and enhance our understanding of how to efficiently update recommendations.

INTRODUCCIÓN: La actualización de recomendaciones de las guías de práctica clínica requiere búsquedas bibliográficas exhaustivas y eficientes. Aunque están disponibles nuevas plataformas de información para grupos desarrolladores, su contribución a este propósito sigue siendo incierta. MÉTODOS: Como parte de una revisión/actualización de 8 recomendaciones basadas en evidencia seleccionadas sobre diabetes tipo 2, evaluamos las siguientes cinco aproximaciones de búsqueda bibliográfica (dirigidas a revisiones sistemáticas, utilizando criterios predeterminados): PubMed para MEDLINE; Epistemonikos utilizando una búsqueda básica; Epistemonikos utilizando una estrategia de búsqueda estructurada; plataforma (L.OVE) y TRIP . Tres revisores clasificaron de forma independiente las referencias recuperadas como definitivamente o probablemente elegibles/no elegibles. Aquellas clasificadas en las mismas categorías "definitivas" para todos los revisores, se etiquetaron como "verdaderas" positivas/negativas. El resto se sometieron a una nueva evaluación y, si se consideraban por consenso elegibles/no elegibles, se convirtieron en "falsos" negativos/positivos, respectivamente. Describimos el rendimiento de cada aproximación, junto a sus medidas de "precisión diagnóstica" y las estadísticas de acuerdo. RESULTADOS: En conjunto, las cinco aproximaciones identificaron 318-505 referencias para las 8 recomendaciones, de las cuales los revisores consideraron elegibles el 4,2 a 9,4% tras las dos rondas. Mientras que Pubmed superó a las otras aproximaciones (odds ratio de diagnóstico 12,5 versus 2,6 a 53), ninguna aproximación de búsqueda identificó por sí misma referencias elegibles para todas las recomendaciones. Individualmente, las búsquedas identificaron hasta el 40% de todas las referencias elegibles (n=71), y ninguna combinación de cualquiera de los tres enfoques pudo identificar más del 80% de ellas. Las estadísticas Kappa para la recuperación entre búsquedas fueron muy pobres (9 de cada 10 comparaciones pareadas no superaron el acuerdo esperado por azar). CONCLUSIONES: Entre las plataformas de información evaluadas, Pubmed parece ser la más eficiente para actualizar este conjunto de recomendaciones. Sin embargo, la escasa concordancia en el rendimiento de las referencias exige que los grupos desarrolladores incorporen información de varias fuentes (probablemente más de tres) para este fin. Es necesario seguir investigando para replicar nuestros hallazgos y mejorar nuestra comprensión de cómo actualizar recomendaciones de forma eficiente.

Molecular robotic agents that survey molecular landscapes for information retrieval.

DNA-based artificial motors have allowed the recapitulation of biological functions and the creation of new features. Here, we present a molecular robotic system that surveys molecular environments and reports spatial information in an autonomous and repeated manner. A group of molecular agents, termed 'crawlers', roam around and copy information from DNA-labeled targets, generating records that reflect their trajectories. Based on a mechanism that allows random crawling, we show that our system is capable of counting the number of subunits in example molecular complexes. Our system can also detect multivalent proximities by generating concatenated records from multiple local interactions. We demonstrate this capability by distinguishing colocalization patterns of three proteins inside fixed cells under different conditions. These mechanisms for examining molecular landscapes may serve as a basis towards creating large-scale detailed molecular interaction maps inside the cell with nanoscale resolution.

Transforming Tele-Ophthalmology: Utilizing Cloud Computing for Remote Eye Care.

BACKGROUND: Tele-ophthalmology is gaining recognition for its role in improving eye care accessibility via cloud-based solutions. The Google Cloud Platform (GCP) Healthcare API enables secure and efficient management of medical image data such as high-resolution ophthalmic images. OBJECTIVES: This study investigates cloud-based solutions' effectiveness in tele-ophthalmology, with a focus on GCP's role in data management, annotation, and integration for a novel imaging device. METHODS: Leveraging the Integrating the Healthcare Enterprise (IHE) Eye Care profile, the cloud platform was utilized as a PACS and integrated with the Open Health Imaging Foundation (OHIF) Viewer for image display and annotation capabilities for ophthalmic images. RESULTS: The setup of a GCP DICOM storage and the OHIF Viewer facilitated remote image data analytics. Prolonged loading times and relatively large individual image file sizes indicated system challenges. CONCLUSION: Cloud platforms have the potential to ease distributed data analytics, as needed for efficient tele-ophthalmology scenarios in research and clinical practice, by providing scalable and secure image management solutions.