Treatment Patterns and Outcomes of Preoperative Neoadjuvant Radiotherapy in Patients with Early-onset Rectal Cancer
Bibliographic record
Abstract
Preoperative radiotherapy for early-stage rectal cancer has risks and benefits that may impact treatment choice in young patients. We reviewed radiotherapy use and outcomes for rectal cancer by age. Patients with early-stage rectal cancer in the Canadian province of British Columbia from 2002 to 2016 were identified (n = 6,232). Baseline characteristics, treatment response, overall survival (OS), disease-free survival (DFS), disease-specific survival (DSS), and locoregional recurrence rate (LRR) were compared between patients <50 (early-onset; n = 532) and ≥50 years old (average-onset; n = 5,700). Early-onset patients were more likely to receive preoperative chemoradiotherapy than short-course radiotherapy [OR, 2.20; 95% confidence interval (CI), 1.67–2.89; P < 0.0001], but also had higher nodal (P = 0.00096) and overall clinical staging (P = 0.033). Cancer downstaging and pathologic complete response rates were similar in those receiving neoadjuvant chemoradiotherapy by age. Early-onset and average-onset patients had similar DSS (P = 0.91) and DFS (P = 0.27) in multivariate analysis unless non-colorectal deaths, which were higher in older patients, were censored in the DFS model (HR, 1.30; 95% CI, 1.01–1.68; P = 0.042). LRR also did not differ between age groups (P = 0.88). Outcomes did not differ based on radiotherapy type. Young patients with rectal cancer are more likely to present with higher staging and receive long-course chemoradiotherapy. DSS did not differ by age group; however, young patients had worse DFS when we censored competing risks of death in older patients. Significance: This population-based study suggests younger patients are more likely to receive chemoradiotherapy, potentially due to higher stage at diagnosis, and response is comparable by age.
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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 | 0.000 |
| 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".