Impact of Postoperative Adjuvant Chemotherapy Following Long-course Chemoradiotherapy in Stage II Rectal Cancer
Bibliographic record
Abstract
OBJECTIVES: Use of adjuvant chemotherapy (AC) following neoadjuvant chemoradiation (nCRT) is controversial in rectal cancer (RC). We assessed a multi-institutional database to determine if there was benefit from AC for pathologic stage II RC patients and whether the addition of oxaliplatin to fluoropyrimidine (OXAC) therapy impacted outcomes. MATERIALS AND METHODS: We included patients who underwent nCRT and had pathologic stage II (ypT3/4 ypN0) tumors. Disease-free survival and overall survival were assessed. Multivariate Cox models adjusting for age, sex, Eastern Cooperative Oncology Group, high-risk features (pT4, poor differentiation, <12 nodes removed, lymphovascular/perineural invasion, or obstruction/perforation), and clinical stage were constructed. RESULTS: Of 485 patients, 73.6% received AC, of which 25.5% received OXAC. Patients receiving AC were younger (median age 61 vs. 64; P=0.003) and had higher rates of total mesorectal excision (81.5% vs. 78.9%; P=0.049), but had similar high-risk features, performance status, clinical stage, margin status, preoperative carcinoembryonic antigen, and nCRT regimen. In univariate analysis, overall survival was improved with fluoropyrimidine AC compared with no AC or OXAC (P=0.049), but not disease-free survival (P=0.33). In multivariate analysis, any AC, fluoropyrimidine AC, or OXAC did not improve outcomes. After stratifying patients by the presence of high-risk features, elevated carcinoembryonic antigen, margin status, or preoperative clinical stage, we did not identify a group with improved outcomes following AC. CONCLUSIONS: In this multi-institutional cohort of yp stage II RC patients, we failed to identify a group that derives benefit from AC following nCRT. The addition of oxaliplatin did not appear to improve outcomes when compared with fluoropyrimidine alone.
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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) | 0.000 | 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".