How to conduct an annual literature update for top articles relevant to clinical practice in geriatrics: A scoping review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.414 | 0.641 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.102 | 0.049 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.029 | 0.040 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.014 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".