Robotic vs other surgery techniques for mitral valve repair and/or replacement: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Mitral valve repair or replacement (MVr/R) are procedures that aim to correct mitral regurgitation. The three techniques, namely conventional, minimally invasive, and robotic each present their advantages and setbacks. Previous studies had compared each technique with the other but mostly focused on two techniques. In this systematic review and meta-analysis, we attempt to compare all three techniques, to provide a reference for the clinical selection of the best surgical scheme. METHODS: , 2022. Critical appraisal of studies was performed using Newcastle Ottawa Scale converted by Agency for Healthcare Research and Quality (AHRQ). We used bayesian network meta-analysis and conventional meta-analysis (random effects model) to rank and analyze pooled odds ratios (OR) and mean differences (MD) with 95% confidence intervals (CI). Forest plots of pooled effect estimates comparing each treatment and ranking panel using Surface Under the Cumulative Ranking (SUCRA) were used for the intervention measures. RESULTS: A total of 18 studies with 60,331 patients were included in this systematic review and meta-analysis. Hospital stay was significantly lower in the group with robotic procedure compared to the conventional interventions in terms of ICU stay and overall length of stay. The mean difference of length of hospital stay days of the conventional group was 2.27 (1.31-3.30) days and of the minimally invasive -0.364 (-2.31-1.53) days compared to the robotic group. The robotic procedure was associated with longer cross-clamp and cardiopulmonary bypass (CPB) times. Nevertheless, the robotic procedure was associated with lower infection (OR = 0.60 [95% CI 0.50-0.73)] rates and in-hospital mortality compared to conventional techniques (OR=0.53 [95% CI 0.40-0.70)] but not the minimally invasive techniques (OR = 1.74 [95% CI 0.48-6.31]). CONCLUSION: Robotic surgery showed more favorable surgical outcomes, including hospital stay, post-operational complications and in-hospital mortality, although it was associated with longer cross-clamp time and CPB time compared to other interventions. However, its high cost is a difficult consideration for its widespread clinical implementation.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".