Bibliographic record
Abstract
To assess the quality of health care, patient outcomes associated with medical providers are routinely monitored in order to identify poor (or excellent) provider performance. To avoid confounding by risk factors, both indirect and direct standardization have been used for comparing outcome rates or prevalence for different providers. There has been an ongoing debate as to which standardization method is more appropriate. To compare the performance of indirect and direct standardization for the purpose of ranking transplant centers, we analyzed post-transplant mortality using the national kidney transplant data. Included in our analysis were 116,601 patients (from 230 transplant centers) who underwent kidney transplantation between January 2006 and December 2012. Multivariate logistic regression model was used to model the 30-day mortality, which were estimates of failures (grant failure or death) in the 30 days after the transplant surgery. Concordance indexes, kappa coefficients and Spearman’s rank correlation coefficient were computed. The estimated values from these statistics for the indirect standardized method were similar to the direct standardization. The results suggest that both indirect and direct standardized methods provide similar ability to distinguish center effects.
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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 | 0.041 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".