Data from Treatment Patterns and Outcomes of Pre-operative Neoadjuvant Radiotherapy in Patients with Early-onset Rectal Cancer
Bibliographic record
Abstract
Abstract Pre-operative 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 2002-2016 were identified (n=6232). 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=5700). Early-onset patients were more likely to receive pre-operative chemoradiotherapy than short-course radiotherapy (OR: 2.20, 95% 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 pCR 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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".