Redo Pelvic Surgery and Combined Metastectomy for Locally Recurrent Rectal Cancer with Known Oligometastatic Disease: A Multicentre Review
Bibliographic record
Abstract
INTRODUCTION: Historically, surgical resection for patients with locally recurrent rectal cancer (LRRC) had been reserved for those without metastatic disease. 'Selective' patients with limited oligometastatic disease (OMD) (involving the liver and/or lung) are now increasingly being considered for resection, with favourable five-year survival rates. METHODS: A retrospective analysis of consecutive patients undergoing multi-visceral pelvic resection of LRRC with their oligometastatic disease between 1 January 2015 and 31 August 2021 across four centres worldwide was performed. The data collected included disease characteristics, neoadjuvant therapy details, perioperative and oncological outcomes. RESULTS: Fourteen participants with a mean age of 59 years were included. There was a female preponderance (n = 9). Nine patients had liver metastases, four had lung metastases and one had both lung and liver disease. The mean number of metastatic tumours was 1.5 +/- 0.85. R0 margins were obtained in 71.4% (n = 10) and 100% (n = 14) of pelvic exenteration and oligometastatic disease surgeries, respectively. Mean lymph node yield was 11.6 +/- 6.9 nodes, with positive nodes being found in 28.6% (n = 4) of cases. A single major morbidity was reported, with no perioperative deaths. At follow-up, the median disease-free survival and overall survival were 12.3 months (IQR 4.5-17.5 months) and 25.9 months (IQR 6.2-39.7 months), respectively. CONCLUSIONS: Performing radical multi-visceral surgery for LRRC and distant oligometastatic disease appears to be feasible in appropriately selected patients that underwent good perioperative counselling.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".