Redo isolated tricuspid valve surgery: prediction of in-hospital mortality using the TRI-SCORE
Bibliographic record
Abstract
Abstract Background Redo isolated tricuspid valve surgery (ITVS) is rarely performed. The TRI-SCORE reliably predicts in-hospital mortality after ITVS on native valve but has not been tested in the setting of redo interventions. Purpose We aimed to compare the predictive value of the TRI-SCORE to other surgical risk scores for redo ITVS. Methods Using a mandatory administrative database, we identified all consecutive adult patients who underwent a redo ITVS at 12 French tertiary centers between 2007 and 2017. Baseline characteristics and outcomes were collected from chart review and the TRI-SCORE, Logistic EuroSCORE, EuroSCORE II and STS were calculated. Results We identified 70 patients who underwent a redo ITVS (mean age 54±15 years, 63% female). Prior intervention was a repair in 51% and a replacement in 49%. A tricuspid valve replacement was performed in all patients. In-hospital mortality was 10%. The TRI-SCORE was the only risk score associated with in-hospital mortality (p=0.01). Area under the receiver operating characteristic curve for the TRI-SCORE was 0.83, much higher than with logistic EuroSCORE (0.58), EuroSCORE II (0.61) or STS (0.59). The table presents the observed and predicted values of in-hospital mortality according to TRI-SCORE categories. Conclusion The TRI-SCORE accurately predicted in-hospital mortality after redo isolated tricuspid valve surgery and may guide the clinical decision-making process especially as transcatheter therapies are emerging. Funding Acknowledgement Type of funding sources: None.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".