Association between individual surgeon volume and outcome in mitral valve surgery: a systematic review
Bibliographic record
Abstract
BACKGROUND: Surgeon volume has been identified as a possible factor that influences outcomes in mitral valve (MV) surgery. The aim of this study was to systematically review all published studies on the association between individual surgeon volume and outcome in MV surgery. METHODS: PubMed was searched last on 19 November 2020. The reporting of this systematic review was done in accordance with PRISMA guidelines. Manuscripts were eligible when these studied individual surgeon volumes and its association with repair rate, mortality or reoperation. The methodological quality of the studies was assessed with the Newcastle-Ottawa Scale (NOS). Absolute numbers and percentages of the outcome measures, odds ratios (ORs), P values and threshold values regarding surgeon volume were collected. RESULTS: A total of 7 retrospective cohort studies were included in the qualitative analysis with total of 158488 patients. Definitions of surgeon volumes were found to be heterogenic and therefore pooling of data was not possible. Surgeon volume was significantly associated with repair rate (OR =1.25-5.5) and mortality (OR =0.46-0.84 and OR =1.50-2.27 depending on the reference group). Regarding reoperation, results were not consistent and did not always show a significant lower reoperation rate when surgeon volume increased. A mean threshold of minimally 30 MV surgeries per year was found. DISCUSSION: Higher surgeon volume is significantly associated with improved outcomes of repair rate and mortality. MV should preferentially be performed by high-volume surgeons and centralization of MV surgery might be necessary.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".