Locally recurrent rectal cancer: Role of composite resection of extensive pelvic tumors with strategies for minimizing risk of recurrence
Bibliographic record
Abstract
Locally recurrent cancer of the rectum has been under-recognized as a complication, although it affects up to 40% of patients treated with surgery alone. Even in the best centers, rates average 25%. While radiotherapy may reduce recurrence, it is now apparent that total mesorectal excision is the most effective modality, with rates as low as 5%. The dramatic decrease in local recurrence can also be linked to increased survival in prospective studies, an effect more significant than any adjuvant therapy. The options, however, for patients with locally recurrent cancer are limited. Fifteen percent of patients with this complication die without systemic spread. Salvage by surgery offers potential cure. Other than anastomotic recurrences that can be locally resected, the best approach for long-term survival is an extensive surgical procedure requiring en bloc removal of adjacent organs and pelvic structures-so-called composite resection. With careful selection, 30% 5-year survival can be achieved and palliation is considerable, with 50% long-term local control. Intraoperative radiotherapy and brachytherapy, and/or preoperative chemoradiation may provide better results in future. Newer techniques of coloanal anastomosis, improved urinary diversion, and myocutaneous flaps for perineal reconstruction radically reduce the morbidity of these procedures. The approach to recurrent rectal cancer requires a sophisticated multidisciplinary team to obtain optimum results.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".