Presentation of evidence in continuing medical education programs: A mixed methods study *
Bibliographic record
Abstract
INTRODUCTION: Clinical trial data can be presented in ways that exaggerate treatment effectiveness. Physicians consider therapy more effective, and may be more likely to make inappropriate practice changes, when data are presented in relative terms such as relative risk reduction rather than in absolute terms such as absolute risk reduction and number needed to treat. Our purpose was to determine (1) how frequently continuing medical education (CME) speakers present research data in relative terms compared to absolute terms; (2) how knowledgeable CME speakers and learners are about these terms; and (3) how CME learners want these terms presented. METHODS: Analysis of videotapes and PowerPoint slides of 26 CME presentations, questionnaire survey of CME speakers and learners, and focus groups with learners. RESULTS: Speakers presented data more frequently in relative than absolute terms, but most frequently in general terms such as frequencies, percentages, graphs, and P-values with no data. Of 1367 PowerPoint slides, 269 presented research data, and of these, 225 (84%) presented data in general terms, 50 (19%) in relative terms and 19 (7%) in absolute terms. CME speakers understood relative and absolute terms better than learners. Approximately 25-35% of speakers and 45-65% of learners could not correctly calculate relative risk reduction, absolute risk reduction, and number needed to treat. Learners wished to have these terms presented in CME programs in a consistent and easily understood format and requested a brief review of them at the beginning of CME programs. DISCUSSION: Presentation of research data in most CME programs is inadequate to allow learners to make fully informed therapeutic decisions. Speakers and learners need professional development to improve their presentation and understanding of research data.
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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 | 0.239 | 0.398 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".