What Keeps Postpulmonary Resection Patients in Hospital?
Bibliographic record
Abstract
BACKGROUND: Prolonged air leak (longer than three days) was hypothesized to be the primary cause of extended hospital stays following pulmonary resection. Its effect on length of stay (LOS) was compared with that of suboptimal pain control, nausea and vomiting, and other causes. Predictors of prolonged LOS and of prolonged air leaks were investigated. DESIGN: Retrospective review of 91 patients. Primary reasons for prolonged hospitalization were determined. Patient characteristics (demographic information, pulmonary function test results, body habitus measurements, smoking history), operative factors (procedure performed, duration of operation, complications) and postoperative factors (time of chest tube removal) were considered. Student's t test and chi2 analysis were used to compare continuous and ratio data, respectively, and linear regression analysis was used to define the equation relating two variables. RESULTS: The mean postoperative LOS was 6.4 days. Only prolonged air leak was predictive of increased LOS (9.4 days versus 5.4 days, P<0.001). Forced expiratory volume in 1 s less than 1.5 L/min, carbon monoxide diffusing capacity less than 80% predicted and the detection of a pneumothorax were all predictive of prolonged air leak. A strong correlation between the time of chest tube removal and LOS was found (r=0.937, P<0.001). Linear regression analysis showed postoperative LOS and duration of thoracostomy tube insertion to be related by the equation y = 0.88x + 2.49 days. CONCLUSION: These results suggest that increased LOS following pulmonary resection is due primarily to prolonged air leaks. Furthermore, patients who have their chest tubes removed sooner are discharged sooner.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".