Do continuing medical education articles foster shared decision making?
Bibliographic record
Abstract
INTRODUCTION: Defined as reviews of clinical aspects of a specific health problem published in peer-reviewed and non-peer-reviewed medical journals, offered without charge, continuing medical education (CME) articles form a key strategy for translating knowledge into practice. This study assessed CME articles for mention of evidence-based information on benefits and harms of available treatment and/or preventive options that are deemed essential for shared decision making (SDM) to occur in clinical practice. METHODS: Articles were selected from 5 medical journals that publish CME articles and are provided free of charge to primary-care physicians of the Province of Quebec, Canada. Two individuals independently scored each article with the use of a 10-item checklist based on the International Patient Decision Aid Standards. In case of discrepancy, the item score was established by team consensus. Scores were added to produce a total article score ranging from 0 (no item present) to 10 (all items present). RESULTS: Thirty articles (6 articles per journal) were selected. Total article scores ranged from 1 to 9, with a mean (+/- SD) of 3.1 +/- 2.0 (95% confidence interval 2.8-4.3). Health conditions and treatment options were the items most frequently discussed in the articles; next came treatment benefits. Possible harms, the use of the same denominators for benefits and harms, and methods to facilitate the communication of benefits and harms to patients were almost never described. No significant differences between journals were observed. DISCUSSION: The CME articles evaluated did not include the evidence-based information necessary to foster SDM in clinical practice. Peer-reviewed and non-peer-reviewed medical journals should require CME articles to include this type of information.
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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 | 0.087 | 0.526 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".