The Effect of Major and Minor Complications After Lung Surgery on Length of Stay and Readmission
Bibliographic record
Abstract
The effect of post-operative adverse events (AEs) on patient outcomes such as length of stay (LOS) and readmissions to hospital is not completely understood. This study examined the severity of AEs from a high-volume thoracic surgery center and its effect on the patient postoperative LOS and readmissions to hospital. This study includes patients who underwent an elective lung resection between September 2018 and January 2020. The AEs were grouped as no AEs, 1 or more minor AEs, and 1 or more major AEs. The effects of the AEs on patient LOS and readmissions were examined using a survival analysis and logistic regression, respectively, while adjusting for the other demographic or clinical variables. Among 488 patients who underwent lung surgery, (Wedge resection [n = 100], Segmentectomy [n = 51], Lobectomy [n = 310], Bilobectomy [n = 10], or Pneumonectomy [n = 17]) for either primary (n = 440) or secondary (n = 48) lung cancers, 179 (36.7%) patients had no AEs, 264 (54.1%) patients had 1 or more minor AEs, and 45 (9.2%) patients had 1 or more major AEs. Overall, the median of LOS was 3 days which varied significantly between AE groups; 2, 4, and 8 days among the no, minor, and major AE groups, respectively. In addition, type of surgery, renal disease (urinary tract infection [UTI], urinary retention, or acute kidney injury), and ASA (American Society of Anesthesiology) score were significant predictors of LOS. Finally, 58 (11.9%) patients were readmitted. Readmission was significantly associated with AE group ( P = 0.016). No other variable could significantly predict patient readmission. Overall, postoperative AEs significantly affect the postoperative LOS and readmission rates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".