Should Laparoscopic Complete Mesocolic Excision Be Offered to Elderly Patients to Treat Right-Sided Colon Cancer?
Bibliographic record
Abstract
Background: Despite its potential oncologic benefit, complete mesocolic excision (CME) has rarely been offered to elderly patients. The present study evaluated the effect of age on postoperative outcomes among patients undergoing laparoscopic right colectomies with CME for right-sided colon cancer (RCC). Methods: Data of patients undergoing laparoscopic right colectomies with CME for RCC between 2015 and 2018 were retrospectively analyzed. Selected patients were divided into two groups: the under-80 group and the over-80 group. Surgical, pathological, and oncological outcomes among the groups were compared. Results: A total of 130 patients were selected (95 in the under-80 group and 35 in the over-80 group). No difference was found between the groups in terms of postoperative outcomes, except for median length of stay and adjuvant chemotherapy received, which were in favor of the under-80 group (5 vs. 8 days, p < 0.001 and 26.3% vs. 2.9%, p = 0.003, respectively). No difference between the groups was found regarding overall survival and disease free survival. Using multivariate analysis, only the ASA score > 2 (p = 0.01) was an independent predictor of overall complications. Conclusions: laparoscopic right colectomy with CME for RCC was safely performed in elderly patients ensuring similar oncological outcomes compared to younger patients.
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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 | 0.001 |
| 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".