Inadequate Presentation of Evidence in an Internal Medicine Conference
Bibliographic record
Abstract
Background Studies have found that physicians are more likely to consider therapy effective when information is presented in relative terms (e.g., RRR, OR, HR) rather than in absolute terms (ARR, NNT). In an earlier study of family physician (FP) therapeutics conferences, we found that 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. Objectives To study a national internal medicine conference and determine 1) how completely research data supporting therapeutic recommendations is reported in relative and absolute terms; and 2) how well learners and speakers understand relative and absolute terms. Methods We videotaped and analyzed 14 presentations from the 2011 Canadian Society of Internal Medicine Annual Scientific Meeting. Learners and teachers at the meeting completed an online statistical comprehension survey. Results Of 549 slides we analyzed, 148 made therapeutic recommendations and 145 presented research data. Of those 145 slides, 81% presented data in general terms, 31% in relative terms, and 3% in absolute terms. For RRR, ARR, NNT and CI, approximately 40% of learners and 50% to 70% of speakers considered they understood these terms well enough to explain to them to others. Approximately 35% of learners and 43% of speakers answered questions about RRR, ARR, NNT, OR and HR correctly. Conclusions Learners who attended this conference were not provided with the statistical information they needed to make fully informed therapeutic decisions. There was inadequate knowledge of basic statistical terms among both learners and teachers.
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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 | 0.098 | 0.430 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".