An international survey-based assessment of minimally invasive mitral valve surgery
Bibliographic record
Abstract
OBJECTIVES: Minimally invasive mitral valve surgery (MIMVS) has been shown to be safe and feasible however its adoption has lagged globally. The international consortium is lacking a set of guidelines that are specific to MIMVS. The aim of this study was to capture the practices of MIMVS in different centres. METHODS: A survey was constructed containing 52 multiple-choice and open-ended questions about various aspects of MIMVS. The survey was sent to centres that routinely and frequently perform MIMVS. All surgeons provided informed consent for participating in the survey and publication of data. RESULTS: The survey was sent to 75 known surgeons from whom 32 (42%) completed the survey. All survey responders performed >25 MIMVS cases annually. Twenty (68%) of the surgeons thought that simulation training, MIMVS fellowship and proctorship are all essential prior to commencing an MIMVS program. Eleven (34%) of the surgeons stated that 50-100 MIMVS cases are required to overcome the learning curve, followed by 6 (18%) who said 21-30 cases should suffice. Eighteen (62%) of the surgeons had adopted a fully endoscopic approach for their MIMVS, followed by 15 (51%) surgeons who had performed cases via endoscopic-assisted strategies, 5 (17%) surgeons had conducted the operation under direct visualization and 6 (20%) surgeons had used a robot for their MIMVS. CONCLUSIONS: The study highlights a marked variability on training and approach to MIMVS. Consensus guidelines should be established to allow standardization of MIMVS.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".