Comparative outcomes of minimally invasive right anterior mini-thoracotomy vs conventional sternotomy in aortic valve replacement: a propensity matched meta-analysis
Bibliographic record
Abstract
Background: Aortic valve replacement (AVR) is the standard intervention for treating aortic valve pathologies like aortic stenosis, with conventional sternotomy (CS) being the standard approach. Despite its efficacy, it is associated with post-procedural complications. Hence, a novel minimally invasive procedure called right anterior mini-thoracotomy (RAMT) has emerged, minimizing surgical trauma and enhancing recovery. The emergence of RAMT offers new dimensions to surgical decision-making. Methods: Electronic databases such as PubMed, Cochrane Library, and ScienceDirect were searched from inception to June 2024 for propensity matched studies comparing RAMT with CS for AVR. The Newcastle-Ottawa Scale was used to assess the quality of included studies and to assess the certainty of the outcomes measured, GRADE assessment was performed. Statistical analysis was performed using RevMan (version 5.4.1), and risk ratio (RR) and weighted mean difference (WMD) with 95% CIs were utilized using the random effects model. Results: = 0.03). Conversely, outcomes such as hospital stay, reexploration for bleeding, aortic clamping time, ICU length of stay, atrial fibrillation, infection, stroke, and bypass time demonstrated no statistically significant differences between RAMT and CS. Conclusions: Our study found RAMT a suitable alternative as it effectively reduced early mortality and ventilation time; however, high heterogeneity among the studies and limited data suggest that further research is warranted to confirm its efficacy.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.042 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".