International Preoperative Rectal Cancer Management: Staging, Neoadjuvant Treatment, and Impact of Multidisciplinary Teams
Bibliographic record
Abstract
BACKGROUND: Little is known regarding variations in preoperative treatment and practice for rectal cancer (RC) on an international level, yet practice variation may result in differences in recurrence and survival rates. METHODS: One hundred seventy-three international colorectal centers were invited to participate in a survey of preoperative management of rectal cancer. RESULTS: One hundred twenty-three (71%) responded, with a majority of respondents from North America, Europe, and Asia. Ninety-three percent have more than 5 years' experience with rectal cancer surgery. Fifty-five percent use CT scan, 35% MRI, 29% ERUS, 12% digital rectal examination and 1% PET scan in all RC cases. Seventy-four percent consider threatened circumferential margin (CRM) an indication for neoadjuvant treatment. Ninety-two percent prefer 5-FU-based long-course neoadjuvant chemoradiation therapy (CRT). A significant difference in practice exists between the US and non-US surgeons: poor histological differentiation as an indication for CRT (25% vs. 7.0%, p = 0.008), CRT for stage II and III rectal cancer (92% vs. 43%, p = 0.0001), MRI for all RC patients (20% vs. 42%, p = 0.03), and ERUS for all RC patients (43% vs. 21%, p = 0.01). Multidisciplinary team meetings significantly influence decisions for MRI (RR = 3.62), neoadjuvant treatment (threatened CRM, RR = 5.67, stage II + III RR = 2.98), quality of pathology report (RR = 4.85), and sphincter-saving surgery (RR = 3.81). CONCLUSIONS: There was little consensus on staging, neoadjuvant treatment, and preoperative management of rectal cancer. Regular multidisciplinary team meetings influence decisions about neoadjuvant treatment and staging methods.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".