Quality of Colon Cancer Care in Patients Undergoing Emergency Surgery
Bibliographic record
Abstract
Thirty percent of colon cancer diagnoses occur following emergency presentations, often with bowel obstruction or perforation requiring urgent surgery. We sought to compare cancer care quality between patients receiving emergency versus elective surgery. We conducted an institutional retrospective matched (46 elective:23 emergency; n = 69) case control study. Patients who underwent a colon cancer resection from January 2017 to February 2019 were matched by age, sex, and cancer stage. Data were collected through the National Surgical Quality Improvement Program and chart review. Process outcomes of interest included receipt of cross-sectional imaging, CEA testing, pre-operative cancer diagnosis, pre-operative colonoscopy, margin status, nodal yield, pathology reporting, and oncology referral. No differences were found between elective and emergency groups with respect to demographics, margin status, nodal yield, oncology referral times/rates, or time to pathology reporting. Patients undergoing emergency surgery were less likely to have CEA levels, CT staging, and colonoscopy (p = 0.004, p = 0.017, p < 0.001). Emergency cases were less likely to be approached laparoscopically (p = 0.03), and patients had a longer length of stay (p < 0.001) and 30-day readmission rate (p = 0.01). Patients undergoing emergency surgery receive high quality resections and timely post-operative referrals but receive inferior peri-operative workup. The adoption of a hybrid acute care surgery model including short-interval follow-up with a surgical oncologist or colorectal surgeon may improve the quality of care that patients with colon cancer receive after acute presentations. Surgeons treating patients with colon cancer emergently can improve their care quality by ensuring that appropriate and timely disease evaluation is completed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".