The Influence of Display and Statistical Factors on the Interpretation of Metaanalysis Results by Physicians
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to determine the extent to which various factors affect the interpretation of metaanalytic results by physicians. STUDY DESIGN: A sample of 120 physicians, selected from The Royal College of Physicians and Surgeons of Canada (RCPSC), was randomly assigned to 1 of 6 groups (n = 20) created from a combination of 3 summary measures and 2 levels of disease severity. The intervention consisted of a written scenario and 4 individual displays of metaanalyses (M-A), each followed by questions related to the interpretation of results of M-A. Two final questions examined statistical familiarity/proficiency with the summary measures used. DATA ANALYSIS: Analyses of variance examined main effects and interactions among 4 factors: summary measure, disease severity, effect size, and statistical consistency of the studies comprising the metaanalysis. Two outcomes were examined: interpretation of the treatment effect and confidence in the interpretation of the treatment effect. PRINCIPAL FINDINGS: Physicians were more likely to favor treatment when the results of the primary randomized, controlled trials (RCTs) were statistically homogeneous (P = 0.001) and when the overall effect size was large (P = 0.001). Also, physicians were more likely to be confident when the results were homogeneous (P = 0.001) and when effect size was large (P = 0.000). Interactions also revealed that the effect of statistical consistency of contributing to RCTs was greatest when data were presented as risk difference for treatment outcome (P = 0.026) and when effect size was small (P = 0.000). CONCLUSIONS: The interpretation of metaanalytic displays is influenced by the overall effect size of M-A, the statistical consistency of the contributing RCTs, and interactions of these factors with display factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".