Delayed hospitalisation for heart failure after transcatheter repair or medical treatment for secondary mitral regurgitation: a landmark analysis of the MITRA-FR trial
Bibliographic record
Abstract
BACKGROUND: In the MITRA-FR trial, transcatheter mitral valve repair (TMVR) was not associated with a 2-year clinical benefit in patients with secondary mitral regurgitation (SMR). AIMS: This landmark analysis aimed at investigating a potential reduction of the hospitalisation rate for heart failure (HF) between 12 and 24 months after inclusion in the MITRA-FR trial in patients randomised to the intervention group (TMVR with the MitraClip device), as compared with patients randomised to the control group (guideline-directed medical therapy [GDMT]). METHODS: The MITRA-FR trial randomised 307 patients with SMR for TMVR on top of GDMT (TMVR group; n=152) or for GDMT alone (control group; n=155). We conducted a 12-month landmark analysis in surviving patients who were not hospitalised for HF within the first 12 months of follow-up. The primary endpoint was the 1-year cumulative number of HF hospitalisations. RESULTS: A total of 140 patients (TMVR group: 67; GDMT group: 73) were selected for this landmark analysis with similar characteristics at inclusion in the trial. The primary endpoint was 28 events per 100 patient-years in the TMVR group, as compared with 60 events per 100 patient-years in the GDMT group (hazard ratio [HR] 0.46, 95% confidence interval [CI]: 0.20-1.02; p=0.057). CONCLUSIONS: In this landmark analysis of the MITRA-FR trial, the cumulative rate of HF hospitalisation between 12 and 24 months among patients treated with TMVR on top of GDMT was approximately half as many as those of patients treated with GDMT alone, a difference which did not reach statistical significance in the setting of a low number of events.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".