Learning curve predictors for minimally invasive mitral valve surgery; how far should the rabbit hole go?
Bibliographic record
Abstract
OBJECTIVE: To analyze predictors that influence the learning curve of minimally invasive mitral valve surgery (MIMVS). METHODS: Patients who underwent MIMVS between March 2010 to March 2015 were retrospectively analyzed. Predictive factors that influence the learning curve were analyzed. RESULTS: One hundred and five patients were included in the analysis. Cardiopulmonary bypass (CPB) time in minutes was 158.72 ± 40.98 and the aortic cross-clamp (ACC) time in minutes was 114.48 ± 27.29. There were three operative mortalities, one stroke and five >2+ mitral regurgitation. ACC time in minutes was higher in the low logistic Euroscore II (LES) group (LES < 5% = 118.42 ± 27.94) versus (LES ≥ 5 = 88.66 ± 22.26), P < .05 while creatinine clearance in μmol/L was higher in the LES < 5% group (LES < 5% = 84.32 ± 33.7) versus (LES ≥ 5% = 41.66 ± 17.14), (P < .05). One patient from each group required chest tube insertion for pleural effusion P < .05. The cumulative sum analysis (CUSUM) for the first 25 patients had CPB and ACC times that reached the upper limits. Between 25 to 64 patients the curve remained stable while with the introduction of reoperations and complex surgical procedures the CUSUM reached the upper limits. CONCLUSIONS: The learning curve is affected by many factors but this should not desist surgeons from approaching this technique. The introduction of high-risk patients in clinical practice should be carefully measured based on surgeon experience.
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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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".