Preoperative and intraoperative factors predictive of length of hospital stay after pulmonary lobectomy.
Bibliographic record
Abstract
BACKGROUND: Length of hospital stay is an important determinant of overall surgical costs. Health care resources are finite, so reductions in length of stay are desirable. We reviewed our experience with pulmonary lobectomy to identify preoperative and intraoperative factors that predicted the length of postoperative hospital stay. By identifying these factors, we hoped to favorably influence future patient management. METHODS: Records of patients undergoing pulmonary lobectomy for benign or malignant disease over a four-year period (1998-2001) were reviewed. Data was collected on age, sex, pulmonary function, pulmonary pathology, cigarette smoking, type of thoracotomy incision, use of surgical sealants, surgeon, and length of hospital stay. RESULTS: Three hundred and sixty patients underwent lobectomy. Forward stepwise regression identified age (p=0.022), FEV1 (forced expiratory volume in one second) (p=0.047), diffusion capacity (p=0.020), and surgeon (p<0.001) as independent factors predictive of hospital length of stay. When these four factors were analyzed in a multiple linear regression model, the surgeon variable emerged as the strongest predictor of length of stay (p<0.001). CONCLUSIONS: Although patient factors were influential, the individual surgeon was the most important determinant of hospital length of stay after pulmonary lobectomy. It may be possible to reduce length of hospital stay by identifying variations in practice within the surgical group, and encouraging widespread adoption of "best practice" surgical techniques.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".