A population‐based comparison of 30‐day readmission after surgery for colon and rectal cancer: How are they different?
Bibliographic record
Abstract
BACKGROUND: An implicit assumption in the analysis of colorectal readmission is that colon and rectal cancer patients are similar enough to analyze together. However, no studies have examined this assumption and whether substantial differences exist between colon and rectal cancer patients. METHODS: This was a retrospective analysis of the differences in predictors, diagnoses, and costs of readmission between colon and rectal cancer cohorts for 30-day readmission. This study included all patients aged >18 who received an elective colectomy or low anterior resection for colorectal cancer from April 2008 until March 2012 in the province of Ontario. RESULTS: Overall, 13,571 patients were identified and the readmission rates significantly differed between rectal and colon cancer patients (7.1% colon and 10.7% rectal P = 0.001). Diabetes, age, and discharge to long term care were significantly different among colon and rectal patients in the prediction of readmission. Readmission for renal and stoma causes was more prominent in the rectal cohort. The adjusted cost difference for readmission did not significantly differ between rectal and colon cancer $178 ($1,924-1,568 P = 0.84) CONCLUSION: Several important differences in predictors and diagnoses exist between the two cohorts. Conversely, the costs associated with readmission were homogenous between rectal and colon cancer patients. J. Surg. Oncol. 2016;114:354-360. © 2016 Wiley Periodicals, Inc.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".