MétaCan
Menu
← Back to cohort
Record W4390707095 · doi:10.1111/jgs.18737

How to conduct an annual literature update for top articles relevant to clinical practice in geriatrics: A scoping review

2024· review· en· W4390707095 on OpenAlexaff
Janice Lee, Elizabeth Uleryk, Savithiri Ratnapalan

Bibliographic record

VenueJournal of the American Geriatrics Society · 2024
Typereview
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsContext (archaeology)GeriatricsMedicineRelevance (law)MEDLINECritical appraisalSelection (genetic algorithm)BibliometricsMedical educationSystematic reviewAlternative medicineLibrary scienceComputer sciencePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Clinical educators in geriatrics are often tasked with presenting a literature update at annual conferences and scientific meetings, which is a highly regarded continuing medical education (CME) activity. Preparation of an annual literature update cannot rely on bibliometric analysis due to time lag and poor correlation between bibliometrics and expert opinion on clinical relevance. The methodology of how top research articles of the year are selected and presented is not often reported. METHODS: We conducted a scoping review for published reports of a curated selection of recent articles critically appraised for high impact to clinical practice in general geriatrics, published from 2010 to 2022. RESULTS: Six annual literature updates were included for study. Three updates detailed their article sources, ranging from a survey of clinicians, consulting seven individual journals, searching up to four bibliographic databases, scanning social media outlets, and reviewing previous literature updates. One update reported a detailed method of article selection and consensus development. Critical appraisal of articles followed a structured reporting of clinical context, methods, results, and a statement of clinical implication or bottom line. Three of the six updates' results were disseminated in an annual conference update and did not evaluate learning outcomes of the audience. We mapped the results on a four-step framework of article search, selection, critical appraisal, and dissemination of knowledge. CONCLUSIONS: Educators in geriatrics consult numerous article sources spanning multiple journals, databases, social media, and peer suggestions to create an annual literature update. The methodology of article search and selection is inconsistently described. In this exciting area of CME, we encourage educators to develop a framework for conducting annual literature updates in geriatrics and expand its scholarship.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.414
metaresearch head score (Gemma)0.641
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.586
Threshold uncertainty score0.723

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4140.641
Meta-epidemiology (narrow)0.0050.006
Meta-epidemiology (broad)0.0130.013
Bibliometrics0.1020.049
Science and technology studies0.0080.006
Scholarly communication0.0290.040
Open science0.0080.016
Research integrity0.0140.008
Insufficient payload (model declined to judge)0.0100.008

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.250
GPT teacher head0.616
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designSystematic review
DomainMethods
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Geriatrics Society→Same topicHealth Sciences Research and Education→French-language works237,207→