model outperforms external risk models
Bibliographic record
Abstract
This study aimed at assessing the performance of three external risk-adjusted models – logistic EuroSCORE, Parsonnet score and Ontario Province Risk (OPR) score – in predicting in-hospital mortality in patients submitted to coronary artery bypass graft (CABG) and to develop a local risk-score model. Data on 4567 patients who underwent isolated CABG (1992–2001) were extracted from our clinical database. Hospital mortality was 0.96 % (44 patients). For the three external systems, observed and predicted mortalities were compared, and discrimination and calibration were assessed. A local risk model was developed and validated by means of logistic regression and bootstrap analysis. The EuroSCORE predicted a mortality of 2.34 % (P-0.001 vs. observed), the Parsonnet 4.43 % (P-0.0001) and the OPR 1.66% (P-0.005). All models overestimated mortality significantly in almost all tertile risk groups. The areas under the ROC curve (AUC) for EuroSCORE, Parsonnet and OPR were 0.754, 0.664 and 0.683, respectively. The local model exhibited good calibration and discrimination AUC, 0.752. In conclusion, the three risk-score systems analyzed do not accurately predict in-hospital mortality in our coronary surgery patients; hence their use for risk prediction may not be appropriate in our population. We developed a risk-prediction model that can be used as an instrument to provide accurate information about the risk of in-hospital mortality in our patient population.
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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 | 0.021 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".