Factors Associated with Early Discharge after Non-Emergent Right Colectomy for Colon Cancer: A NSQIP Analysis
Bibliographic record
Abstract
The National Surgical Quality Improvement Project (NSQIP) dataset was used to identify perioperative variables associated with the length of stay (LOS) and early discharge among cancer patients undergoing colectomy. Patients who underwent non-emergent right colectomy for colon cancer from 2012 to 2019 were identified from the NSQIP and colectomy-targeted databases. Postoperative LOS was analyzed based on postoperative day (POD) of discharge, with patients grouped into Early Discharge (POD 0–2), Standard Discharge (POD 3–5), or Late Discharge (POD ≥ 6) cohorts. Multivariable ordinal logistic regression was performed to identify risk factors associated with early discharge. The NSQIP query yielded 26,072 patients: 3684 (14%) in the Early Discharge, 13,414 (52%) in the Standard Discharge, and 8974 (34%) in the Late Discharge cohorts. The median LOS was 4.0 days (IQR: 3.0–7.0). Thirty-day readmission rates were 7% for Early Discharge, 8% for Standard Discharge, and 12% for Late Discharge. On multivariable regression analysis, risk factors significantly associated with a shorter LOS included independent functional status, minimally invasive approach, and absence of ostomy or additional bowel resection (all p < 0.001). Perioperative variables can be used to develop a model to identify patients eligible for early discharge after right colectomy for colon cancer. Efforts to decrease the overall median length of stay should focus on optimization of modifiable risk factors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".