Causes of increased length of hospitalization on a general thoracic surgery service: a prospective observational study.
Bibliographic record
Abstract
OBJECTIVE: To characterize medical and nonmedical reasons for delayed discharge on a general thoracic surgery unit. DESIGN: A prospective observational cohort study. SETTING: A university-affiliated tertiary care institution. PATIENTS: Between February 1999 and July 2000, the in-hospital progress of 130 patients who had undergone an elective thoracic surgical procedure was evaluated prospectively. Baseline characteristics (age, sex, comorbid conditions and pulmonary function test results) were documented. MAIN OUTCOME MEASURES: Complications that delayed the time when the patient was medically ready for discharge. The day the patient was deemed fit for discharge (medically required length of stay) was compared with the actual day of discharge (actual length of stay). RESULTS: The 3 most frequent complications that prevented discharge by postoperative day 6 were persistent air leaks, pulmonary infections and atrial fibrillation. The presence of a persistent air leak increased the medically required length of stay by a mean of 13.1 days (95% confidence interval [CI] 11.0-15.2 d), pneumonia by 9.6 days (95% CI 4.96-14.2 d) and atrial fibrillation by 2.4 days (95% CI -2.6 to 7.4 d). The mean medically required length of stay was 6.9 days, and this differed from the mean day on which the patient was actually discharged (7.35 d, p < 0.01), which contributed 44 excess days of hospitalization per 100 patients. The 2 most common causes of this discrepancy were the lack of home support (10.2% of patients) and the unavailability of convalescent facilities (7.1% of patients). Prolonged hospital stay for nonmedical reasons was associated with increased mean age (67.4 v. 60.7 yr, p = 0.05). CONCLUSIONS: Length of hospitalization after elective thoracic surgery may be prolonged for medical or nonmedical reasons. Although complications like persistent air leak and pneumonia have an impact on medically required length of stay, social factors may also significantly delay discharge.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".