Single-Stage Anterior High Sacrectomy for Locally Recurrent Rectal Cancer
Bibliographic record
Abstract
STUDY DESIGN: A review of prospectively collected data on a consecutive series of patients undergoing single-stage anterior high sacrectomy for locally recurrent rectal carcinoma (LRRC). OBJECTIVE: To determine the clinical outcome of patients who underwent anterior high sacrectomy for LRRC. SUMMARY OF BACKGROUND DATA: High sacrectomy for oncological resection remains technically challenging. Surgery has the potential to achieve cure in carefully selected patients. Complete (R0) tumor excision in LRRC may require sacrectomy. High sacral resections (S3 and above) typically require a combined anterior/supine and posterior/prone procedure. We investigated our experience performing single-stage anterior high sacrectomy for LRRC. METHODS: A consecutive series of patients with LRRC without systemic metastases who underwent resection with curative intent requiring high sacrectomy were identified. A review of a prospectively maintained colorectal and spine cancer database data was performed. An oblique dome high sacral osteotomy was performed during a single-stage anterior procedure. Outcome measures included surgical resection margin status, hospital length of stay, postoperative complications, physical functioning status, and overall survival. RESULTS: Nineteen consecutive patients were treated between 2002 and 2011. High sacrectomy was performed at sacral level S1-S2 in 4 patients, S2-S3 in 9 patients, and through S3 in 6 patients. An R0 resection margin was achieved histologically in all 19 cases. There was 1 early (<30 d) postoperative death (1/19, 5%). At median follow-up of 38 months, 13 patients had no evidence of residual disease, 1 was alive with disease, and 4 had died of disease. Morbidities occurred in 15 of the 19 patients (79%). CONCLUSION: Although high sacrectomy may require a combined anterior and posterior surgical approach, our series demonstrates the feasibility of performing single-stage anterior high sacrectomy in LRRC, with acceptable risks and outcomes compared with the literature. The procedure described by us for LRRC lessens the need for a simultaneous or staged prone posterior resection, with favorable R0 tumor resections, patient survival, and clinical outcomes. LEVEL OF EVIDENCE: N/A.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".