Predictive value of the TRI-SCORE for in-hospital mortality after redo isolated tricuspid valve surgery
Bibliographic record
Abstract
OBJECTIVES: The TRI-SCORE reliably predicts in-hospital mortality after isolated tricuspid valve surgery (ITVS) on native valve but has not been tested in the setting of redo interventions. We aimed to evaluate the predictive value of the TRI-SCORE for in-hospital mortality in patients with redo ITVS and to compare its accuracy with conventional surgical risk scores. METHODS: Using a mandatory administrative database, we identified all consecutive adult patients who underwent a redo ITVS at 12 French tertiary centres between 2007 and 2017. Baseline characteristics and outcomes were collected from chart review and surgical scores were calculated. RESULTS: We identified 70 patients who underwent a redo ITVS (54±15 years, 63% female). Prior intervention was a tricuspid valve repair in 51% and a replacement in 49%, and was combined with another surgery in 41%. A tricuspid valve replacement was performed in all patients for the redo surgery. Overall, in-hospital mortality and major postoperative complication rates were 10% and 34%, respectively. The TRI-SCORE was the only surgical risk score associated with in-hospital mortality (p=0.005). The area under the receiver operating characteristic curve for the TRI-SCORE was 0.83, much higher than for the logistic EuroSCORE (0.58) or EuroSCORE II (0.61). The TRI-SCORE was also associated with major postoperative complication rates and survival free of readmissions for heart failure. CONCLUSION: Redo ITVS was rarely performed and was associated with an overall high in-hospital mortality and morbidity, but hiding important individual disparities. The TRI-SCORE accurately predicted in-hospital mortality after redo ITVS and may guide clinical decision-making process (www.tri-score.com).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".