Intersurgeon variations in postoperative length of stay after video-assisted thoracoscopic surgery lobectomy
Bibliographic record
Abstract
Objectives: To identify factors associated with prolonged postoperative length of stay (LOS) after VATS lobectomy (VATS-L), explore potential intersurgeon variation in LOS and ascertain whether or not early discharge influences hospital readmission rates. Methods: We conducted a retrospective analysis of patients who underwent VATS-L at a single academic center between 2018 and 2021. Each VATS lobectomy procedure was performed by 1 of 7 experienced thoracic surgeons. The primary end point of interest was prolonged LOS, defined as an index LOS >3 days. Results: < .001), patient age (odds ratio [OR], 1.03; 95% CI, 1.02-1.06), operation time (OR, 1.01; 95% CI, 1.01-1.01), postoperative complication (OR, 3.60; 95% CI, 2.45-5.29), and prolonged air leak (OR, 8.95; 95% CI, 4.17-19.23). There was no significant association between LOS and gender, body mass index, coronary artery disease, prior atrial fibrillation, American Society of Anesthesiologists score >3, and prior ipsilateral thoracic surgery or sternotomy. There was no association between LOS ≤3 days and hospital readmission (20 [5.3%] vs 39 [5.9%]; OR, 0.88; 95% CI, 0.50-1.53). Conclusions: An intersurgeon variation in postoperative LOS after VATS-L exists and is independent of patient baseline characteristics or perioperative complications. This variation seems to be more closely related to differences in postoperative management and discharge practices rather than to surgical quality. Postoperative discharge within 3 days is safe and does not increase hospital readmissions.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".