Limited surgery following neoadjuvant treatment among patients with localized rectal cancer; a population-based study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the characteristics and outcomes of patients with localized rectal cancer treated with limited versus radical surgery following neoadjuvant treatment. METHODS: Surveillance, Epidemiology, and End Results (SEER) database has been accessed and patients with localized rectal adenocarcinoma (M0) 2004-2015 who have received neoadjuvant radiation before having some sort of surgery were included. Multivariable Cox regression analysis was then used to assess the impact of the type of resection on overall survival and cancer-specific survival. RESULTS: A total of 19162 individuals who have undergone radical surgery and 450 individuals who have undergone limited surgery were included. Using multivariable logistic regression analysis, the following factors were associated with a higher likelihood of radical surgery: younger age (OR for age ≥65 years versus 17-64 years: 0.430; 95% CI: 0.355-0.520) and clinically node-positive stage (OR: 2.442; 95% CI: 1.858-3.211). Within multivariable Cox regression analysis, limited surgery was associated with worse overall survival (HR: 1.276; 95% CI: 1.092-1.491) but no difference in cancer-specific survival (HR: 0.980; 95% CI: 0.728-1.318). CONCLUSION: Among patients with localized rectal cancer treated with neoadjuvant radiation, there does not seem to be a difference in cancer-specific survival according to the type of surgery (limited versus radical surgery).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".