Do clinicians understand the size of treatment effects? A randomized survey across 8 countries
Bibliographic record
Abstract
BACKGROUND: Meta-analyses of continuous outcomes typically provide enough information for decision-makers to evaluate the extent to which chance can explain apparent differences between interventions. The interpretation of the magnitude of these differences - from trivial to large - can, however, be challenging. We investigated clinicians' understanding and perceptions of usefulness of 6 statistical formats for presenting continuous outcomes from meta-analyses (standardized mean difference, minimal important difference units, mean difference in natural units, ratio of means, relative risk and risk difference). METHODS: We invited 610 staff and trainees in internal medicine and family medicine programs in 8 countries to participate. Paper-based, self-administered questionnaires presented summary estimates of hypothetical interventions versus placebo for chronic pain. The estimates showed either a small or a large effect for each of the 6 statistical formats for presenting continuous outcomes. Questions addressed participants' understanding of the magnitude of treatment effects and their perception of the usefulness of the presentation format. We randomly assigned participants 1 of 4 versions of the questionnaire, each with a different effect size (large or small) and presentation order for the 6 formats (1 to 6, or 6 to 1). RESULTS: Overall, 531 (87.0%) of the clinicians responded. Respondents best understood risk difference, followed by relative risk and ratio of means. Similarly, they perceived the dichotomous presentation of continuous outcomes (relative risk and risk difference) to be most useful. Presenting results as a standardized mean difference, the longest standing and most widely used approach, was poorly understood and perceived as least useful. INTERPRETATION: None of the presentation formats were well understood or perceived as extremely useful. Clinicians best understood the dichotomous presentations of continuous outcomes and perceived them to be the most useful. Further initiatives to help clinicians better grasp the magnitude of the treatment effect are needed.
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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 | 0.165 | 0.351 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".