Risk stratification in cardiac surgical patients on VAD
Bibliographic record
Abstract
Objectives: Our purpose was to access the performance of 6 risk stratification models (EuroScore, Cleveland, Parsonnet, Ontario, French and Pons) to predict mortality in patients with postcardiotomy-syndrome and ventricular assist device (VAD). Material and Methods: The study population consisted of all consecutive adult patients admitted after cardiac surgery with postcardiotomy-syndrome and VAD over a period of more than 10 years. Probabilities of hospital death for patients were estimated by applying the six models and were compared with actual mortality rates. Performance was assessed with the Hosmer-Lemeshow (HL) goodness-of-fit test and receiver operating characteristic (ROC) curves. Results: Between January 1993 and April 2003 a total of 11904 patients underwent cardiac surgery at our institution. 168 patients (1.41%) needed a postoperative VAD. In-hospital mortality rate was 67.3%. HL values were: Cleveland 2.8, French 3.1, Euro 3.6, Ontario 6.7, Pons 8.5 and Parsonnet 11.6. The ROC curve showed the following ranking: Parsonnet 0.73, Pons 0.71, Euro 0.69, French 0.67, Cleveland and Ontario 0.63. Conclusions: All scores showed a good calibration and an acceptable discrimination. Paradoxically, the score with the best calibration (Cleveland) has the lowest discrimination and the score with the best discrimination (Parsonnet) has the lowest calibration. We believe that preoperative risk stratification is not of great value for this subset of patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".