TRI-SCORE: a new risk score for in-hospital mortality prediction after isolated tricuspid valve surgery
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Bibliographic record
Abstract
AIMS: Isolated tricuspid valve surgery (ITVS) is considered to be a high-risk procedure, but in-hospital mortality is markedly variable. This study sought to develop a dedicated risk score model to predict the outcome of patients after ITVS for severe tricuspid regurgitation (TR). METHODS AND RESULTS: All consecutive adult patients who underwent ITVS for severe non-congenital TR at 12 French centres between 2007 and 2017 were included. We identified 466 patients (60 ± 16 years, 49% female, functional TR in 49%). In-hospital mortality rate was 10%. We derived and internally validated a scoring system to predict in-hospital mortality using multivariable logistic regression and bootstrapping with 1000 re-samples. The final risk score ranged from 0 to 12 points and included eight parameters: age ≥70 years, New York Heart Association Class III-IV, right-sided heart failure signs, daily dose of furosemide ≥125 mg, glomerular filtration rate <30 mL/min, elevated bilirubin, left ventricular ejection fraction <60%, and moderate/severe right ventricular dysfunction. Tricuspid regurgitation mechanism was not an independent predictor of outcome. Observed and predicted in-hospital mortality rates increased from 0% to 60% and from 1% to 65%, respectively, as the score increased from 0 up to ≥9 points. Apparent and bias-corrected areas under the receiver operating characteristic curves were 0.81 and 0.75, respectively, much higher than the logistic EuroSCORE (0.67) or EuroSCORE II (0.63). CONCLUSION: We propose TRI-SCORE as a dedicated risk score model based on eight easy to ascertain parameters to inform patients and physicians regarding the risk of ITVS and guide the clinical decision-making process of patients with severe TR, especially as transcatheter therapies are emerging (www.tri-score.com).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it