Comparison of long‐term survival outcome of operative <i>vs</i> nonoperative management of recurrent rectal cancer
Bibliographic record
Abstract
AIM: Complete surgical resection is considered the best treatment for recurrent rectal cancer (RRC). The aim of the study was to compare survival outcomes from operative and nonoperative patients presenting with RRC. METHOD: Patients with RRC whose management was discussed by a tertiary referral specialist multidisciplinary team between January 2007 and August 2011 were identified from a prospectively maintained database. The primary end-point was 3-year overall survival. RESULTS: Of 127 patients with RRC, it was isolated to the pelvis in 105 and associated with distant disease at presentation in 22. From the time of primary surgery to first recurrence, 1-, 3-, 5- and 10-year local recurrence rates were 22%, 72%, 85% and 96%, respectively. The number of operated patients available at 1, 2 and 3 years' follow-up was 53, 34 and 23, respectively. Of 70 patients who underwent pelvic resection for recurrence, 64% received R0, 20% received R1 and 16% received R2 resections. Corresponding 3-year overall survival rates were 69%, 56% and 20% (P=0.011). There was no significant difference in survival between R2 resection and those managed nonoperatively (hazard ratio=1.258; P=0.579). Those undergoing surgery for pelvic recurrence affecting one or more compartments had a worse prognosis than those with single-compartment involvement (hazard ratio=2.640; P=0.027). Three-year local recurrence-free survival was 80% with R0 resection vs 60% with R1 resection. CONCLUSION: Most recurrences occur within 5 years of primary surgery, although some occur up to 10 years later. R0 resection is the treatment of choice. There was no survival benefit of R2 resection over nonresected recurrences.
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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 | 0.001 | 0.002 |
| 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".