Neoadjuvant Therapy and Anastomotic Leak After Tumor-Specific Mesorectal Excision for Rectal Cancer
Bibliographic record
Abstract
PURPOSE: This study was designed to evaluate whether neoadjuvant therapy is a risk factor for anastomotic leakage after rectal cancer surgery. METHODS: A retrospective review of 220 patients who underwent tumor-specific mesorectal excision for rectal cancer from 2000 to 2005 was performed. Risk factors for leak were identified by using a multivariable regression model. RESULTS: A total of 54 patients received neoadjuvant chemoradiation therapy and surgery, whereas 166 received surgery alone. No difference in clinically significant leaks was observed between the two groups (5.6 vs. 6.6 percent, P = 1). A diverting ileostomy was performed in 26.4 percent of patients who received neoadjuvant therapy compared with 9.7 percent for surgery alone (P = 0.0021). Neoadjuvant patients were more likely to have ultralow anastomoses (17.6 vs. 2.5 percent, P < 0.0001). On multivariate analysis, smoking (odds ratio, 6.37 (1.8, 22.2), P = 0.004), difficult anastomosis (odds ratio, 7.66 (1.8, 31.5), P = 0.0048), and low level of anastomosis (<or=4 cm from the verge; odds ratio, 5.28 (1.05, 26.6), P = 0.044) were independently associated with anastomotic leakage. CONCLUSIONS: Significant predictors of anastomotic leak include smoking, difficult anastomosis, and level of anastomosis (
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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 | 0.000 | 0.003 |
| 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) | 0.001 | 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".