Tricuspid valve intervention at the time of mitral valve surgery: a meta-analysis
Bibliographic record
Abstract
OBJECTIVES: The surgical management of tricuspid regurgitation (TR) at the time of mitral valve surgery remains controversial. Our objectives were to determine the safety and efficacy of tricuspid valve (TV) repair during mitral valve surgery in a meta-analysis. METHODS: MEDLINE and EMBASE were searched from 1946 to 2017 for all studies comparing TV repair to no intervention at the time of mitral valve surgery on early and late mortality and late TR. A random-effects meta-analysis of all outcomes was performed. RESULTS: One thousand four hundred and seventeen studies were retrieved and a total of 17 studies [2 randomized clinical trial (n = 211), 11 adjusted observational studies (n = 3848) and 4 unadjusted observational studies (n = 67 010)] that compared TV repair (n = 11 787) to no intervention (n = 56 027) at a mean follow-up of 6.0 ± 0.64 years were included. There was no difference in 30-day/in-hospital mortality between repair and no repair [risk ratio (RR) 1.19, 95% confidence interval (95% CI) 0.70-2.02; P = 0.52]. The incidence of new permanent pacemaker implantation was higher in the TV repair group (RR 2.73, 95% CI 2.57-2.89; P < 0.01). TV repair was protective against late moderate or greater TR [incident rate ratio (IRR) 0.28, 95% CI 0.17-0.47; P < 0.01] and severe TR (IRR 0.38, 95% CI 0.17-0.84). There was a numerically lower rate of late TV reoperation (IRR 0.39, 95% CI 0.12-1.25; P = 0.11) that did not reach statistical significance. Overall, there was no difference in late mortality between the 2 treatments (IRR 0.87, 95% CI 0.63-1.24; P = 0.43). CONCLUSIONS: TV repair appears safe in the perioperative period and may reduce future recurrent TR without any survival benefit.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.051 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".