Primer on Statistical Interpretation or Methods Report Card on Propensity-Score Matching in the Cardiology Literature From 2004 to 2006
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Propensity-score matching is frequently used in the cardiology literature. Recent systematic reviews have found that this method is, in general, poorly implemented in the medical literature. The study objective was to examine the quality of the implementation of propensity-score matching in the general cardiology literature. METHODS AND RESULTS: A total of 44 articles published in the American Heart Journal, the American Journal of Cardiology, Circulation, the European Heart Journal, Heart, the International Journal of Cardiology, and the Journal of the American College of Cardiology between January 1, 2004, and December 31, 2006, were examined. Twenty of the 44 studies did not provide adequate information on how the propensity-score-matched pairs were formed. Fourteen studies did not report whether matching on the propensity score balanced baseline characteristics between treated and untreated subjects in the matched sample. Only 4 studies explicitly used statistical methods appropriate for matched studies to compare baseline characteristics between treated and untreated subjects. Only 11 (25%) of the 44 studies explicitly used statistical methods appropriate for the analysis of matched data when estimating the effect of treatment on the outcomes. Only 2 studies described the matching method used, assessed balance in baseline covariates by appropriate methods, and used appropriate statistical methods to estimate the treatment effect and its significance. CONCLUSIONS: Application of propensity-score matching was poor in the cardiology literature. Suggestions for improving the reporting and analysis of studies that use propensity-score matching are provided.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it