Laparoscopic Resection for Colon Cancer: Would all Patients Benefit?
Bibliographic record
Abstract
PURPOSE: This study was designed to assess whether the exclusion criteria used in the Clinical Outcomes of Surgical Therapy and Colon Cancer Laparoscopic or Open Resection trials affected the generalizability of their findings. METHODS: A prospective database of consecutive laparoscopic resections performed for colon cancer was reviewed. Patients were categorized into two groups: inclusion group and exclusion group, based on the selection criteria used in the Clinical Outcomes of Surgical Therapy and Colon Cancer Laparoscopic or Open Resection trials. Baseline and perioperative data were analyzed by using t-tests, Wilcoxon's rank-sum, chi-squared, and Fisher's exact test. Kaplan-Meier survival curves, followed by adjustment for tumor nodes metastasis stage and age utilizing a Cox proportional hazard model, were performed. RESULTS: The inclusion group had 221 patients and the exclusion group had 166 (median age and gender distribution were similar). The exclusion group had a higher conversion rate (23 vs. 11.3 percent; P=0.0023). There was no difference in intraoperative complications (9 percent for exclusion group vs. 8.6 percent for inclusion group; P=0.8), operative time (180 minutes for exclusion group vs.172 minutes for inclusion group; P=0.24), or postoperative complication rates (33.7 percent for exclusion group vs. 26 percent for inclusion group; P=0.13). No difference was detected in perioperative mortality rates, length of stay, days to diet as tolerated, and adjusted two-year survival. CONCLUSIONS: No differences were found in outcomes between the two groups in terms of operative/postoperative complications, length of stay, perioperative mortality, and two-year survival. It seems that all patients with colon cancer can potentially benefit from a laparoscopic approach.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".