The Impact of Adjuvant Chemotherapy on Clinical Outcomes in Locally Advanced Rectal Cancer: A CHORD Consortium Analysis
Bibliographic record
Abstract
Background: The impact of adjuvant chemotherapy (AC) on outcomes in real-world patients with locally advanced rectal cancer (LARC) remains uncertain. Methods: Consecutive patients with LARC (stage II/III) undergoing neoadjuvant chemoradiation before curative-intent surgery from 2005 to 2013 were identified in the Canadian Health Outcomes Research Database. The impact of AC on clinical outcomes, including disease-free survival (DFS) and overall survival (OS), was evaluated using the Kaplan–Meier method and Cox proportional hazards modeling. Results: A total of 1448 patients had sufficient data available to be included for analysis with 1085 (74.9%) receiving AC. Of AC patients, 40.5% received oxaliplatin-based treatments. With a median follow-up of 66.43 months, the 5-year DFS rate was 67.7% (95% CI: 64.5–70.1%) vs. 58.7% (95% CI: 52.8–64.2%) in the AC group and non-AC group, respectively (p < 0.001). The 5-year OS rate of the whole cohort was 74.3% (95% CI: 71.5–76.85%) while the 5-year OS rate of the AC group was 77.8% (95% CI: 74.7–80.6%) compared with 63.8% (95% CI: 57.9–69.2%) for the non-AC group (p < 0.001). On multivariate analysis, patients who received AC had improved DFS (HR 0.6, 95% CI: 0.49–0.73, p < 0.001) and OS (HR 0.46, 95% CI: 0.36–0.58, p < 0.001). Conclusions: This large multi-institutional database analysis supports the use of AC in real-world LARC patients treated with nCRT followed by surgical resection.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".