Implication of a novel postoperative recovery protocol to increase day 1 discharge rate after anatomic lung resection
Bibliographic record
Abstract
BACKGROUND: Chest-tube drainage and prolonged air leak after anatomic lung resection (ALR) continue to drive admission days for most programs employing minimal access techniques. The aim of the study was to evaluate the impact of a novel postoperative recovery protocol with revised chest tube management strategies to target discharge on post-operative day 1 (POD1) after ALR. METHODS: This is a pilot study investigating a novel enhanced recovery protocol which either allowed chest tube removal on POD1 or ambulatory management with indwelling chest tube using a portable closed drainage system. We included all patients undergoing video-assisted thoracoscopic surgery (VATS)-ALR; exclusion criteria were open surgery, non-anatomic or extended resections. RESULTS: 15% in SP cohort (P<0.001). Median length of stay (LOS) was 1 day [interquartile range (IQR), 1-2 days] in PD cohort, while it was 3 days (IQR, 2-5 days) in SP cohort (P<0.001). There were no significant differences in length of indwelling chest-tube, rate of discharge with chest-tube, post-operative complications, or readmissions. On multivariate analysis, PD pathway as well as short surgical time were significant predictors of discharge on POD1. CONCLUSIONS: Our results indicate that POD1 discharge rates of 72% after VATS-ALR can be safely achieved by a well-developed perioperative care pathway and simple chest tube drainage interventions. Based on these findings we are currently drafting a follow-up study to investigate the possibility of performing ALRs as day surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".