Off the shelf or recalibrate? customizing a risk index for assessing mortality.
Bibliographic record
Abstract
BACKGROUND: Public "report cards" for cardiac surgery have been freely available from a variety of sources. These risk-adjusted indices serve as a means of benchmarking outcomes performances, allowing comparisons of outcomes between surgical programs, and quantifying quality improvement programs. We examined two alternative strategies for using previously developed risk-adjusted mortality models in a community hospital: (1) using the model "off the shelf" (OTS) and (2) recalibrating the existing model (RM) to fit the institution-specific population. METHODS: Six OTS models were used: Parsonnet (PA), Canadian (CA), Cleveland (CL), Northern New England (NNE), New York (NY), and New Jersey (NJ). The RM models were created by each model's independent variables and definitions and adjusting the weighting with logistic regression methods. The accuracy, the C statistic, and the precision of each model were assessed for in-hospital mortality. We compared the OTS version of each model to the RM version with methods detailed by Hanley and McNeil. RESULTS: The RM C statistic was improved for all risk-adjusted models, most notably in the statistical improvement seen in the PA (0.053 improvement) and NJ (0.052 improvement) indices. Statistical gains in precision were also seen in the RM models for the PA, CL, and NNE indices. Conversely, one model, the CA model, was more poorly calibrated in the RM model compared with the OTS model, despite an improved C statistic (0.062). CONCLUSIONS: The RM strategy provides institution-explicit models that demonstrate a higher degree of accuracy and precision than the OTS models.
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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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".