Supplementary Figures E1-E3. Propensity score matching versus coarsened exact matching in observational comparative effectiveness research
Bibliographic record
Abstract
• Supplementary materials – figures:o Figure E1a. Selection process for comparison oneo Figure E1b. Selection process for comparison twoo Figure E2a. Distribution of baseline covariates by PSM caliper width for comparison oneo Figure E2b. Distribution of baseline covariates by PSM caliper width for comparison twoo Figure E3a. Distribution of baseline covariates by CEM strategy for comparison oneo Figure E3b. Distribution of baseline covariates by CEM strategy for comparison two Supplementary materials – figuresFigure E1a. Selection process for comparison oneFigure E1b. Selection process for comparison twoFigure E2a. Distribution of baseline covariates by PSM caliper width for comparison oneFigure E2b. Distribution of baseline covariates by PSM caliper width for comparison twoFigure E3a. Distribution of baseline covariates by CEM strategy for comparison oneFigure E3b. Distribution of baseline covariates by CEM strategy for comparison two Supplementary materials – tablesTable E1a. Characteristics of PSM strategies for comparison oneTable E1b. Characteristics of PSM strategies for comparison twoTable E2a. Coarsening of covariates used in CEM for comparison oneTable E2b. Coarsening of covariates used in CEM for comparison twoTable E3a. Characteristics of CEM strategies for comparison oneTable E3b. Characteristics of CEM strategies for comparison two AbstractAims & Methods: We compared propensity score matching (PSM) and coarsened exact matching (CEM) in balancing baseline characteristics between treatment groups using observational data obtained from a pan-Canadian prostate cancer radiotherapy database. Changes in effect estimates were evaluated as a function of improvements in balance, using results from RCTs to guide interpretation. Results: CEM and PSM improved balance between groups in both comparisons, while retaining the majority of original data. Improvements in balance were associated with effect estimates closer to those obtained in RCTs. Conclusions: CEM and PSM led to substantial improvements in balance between comparison groups, while retaining a considerable proportion of original data. This could lead to improved accuracy in effect estimates obtained using observational data in a variety of clinical situations.
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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 | 0.028 | 0.312 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.759 | 0.106 |
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".