Impact of resection vs respect techniques on left ventricular function after mitral valve repair: a systematic review
Bibliographic record
Abstract
Objective: Mitral valve disease, ranging from degenerative to infective origin, is one of the most prevalent left heart diseases globally and affects a large number of individuals.Conventionally, surgical repair has grown to become the treatment of choice, with two main techniques being resection and respect.Current literature has yet to address which technique is superior to the other, particularly in regard to left ventricular (LV) function.Methods: We performed a systematic review and meta-analysis on three databases with a primary outcome of LV function alongside its' parameters, and a secondary outcome of repair durability, mitral valve gradient, and mortality rates.Meta-analysis was performed using random effects, and results were displayed in forest plots.Risk of bias was conducted using the Newcastle-Ottawa Scale.Results: Six retrospective studies were included, evaluating a total of 3376 patients.Pooled results showed that LV function were preserved equally in both groups, showing no statistically significant differences.The respect group had slightly lower mortality rates in comparison to the resect group, and the repair success rate showed a slight superiority in the respect group.Significant heterogeneity was observed on left atrial diameter (LAD) measurement, indicating variability.The overall differences in LV function coming from both techniques appear intangible.Plenty of consideration must be made beyond LV function in determining which repair technique should be performed on a patient.Conclusion: This study demonstrated that both the resect and respect techniques were found to be equally excellent in preserving left ventricular function after mitral valve repair.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".