Incidence Rates, Treatment, and Survival of Rectal Cancer Among Young Patients
Bibliographic record
Abstract
BACKGROUND: The incidence of colorectal cancer is increasing among young adults in the United States. We aim to investigate the incidence rate, the effect of multimodal therapy, and survival outcomes of rectal cancer in patients under 45 years of age. PATIENTS AND METHODS: Data on young-onset (under 45 y) rectal cancer between 2000 and 2016 was extracted from the Surveillance, Epidemiology, and End Results Registry (SEER). RESULTS: A total of 10,375 patients with young-onset rectal cancer were identified where 54.7% were male. The median age at diagnosis was 40±5.7 years. The overall age-adjusted incidence of rectal cancer between 2000 and 2016 was 1.24 per 100,000 per year. Incidence increased with age, with the highest incidence occurring in the 40- to 44-year age group. Over the 16-year study period, rectal cancer increased by ∼2.29%. Most tumors on presentation were moderately differentiated (30.8%) while the most common stage at presentation was stage 4 (48.3%). One- and 5-year cause-specific survival for rectal cancer was 93% and 72%, respectively. According to Cox proportional hazard models, chemotherapy was associated with increased mortality in patients with localized cancer [hazard ratio (HR)=2.88, 95% confidence interval (CI): 2.04-4.08, P<0.001], did not significantly improve mortality outcomes in patients with regional cancer (HR=0.89, 95% CI: 0.70-1.04, P=0.116), but reduced mortality in patients with distant cancer (HR=0.62, 95% CI: 0.56-0.70, P<0.001), though this effect was largely seen in patients 35 years and older. Surgery was associated with improved survival across all cancer stages. CONCLUSIONS: The incidence of regional and distant rectal cancer is increasing in young patients. While patient age is an important prognostic indicator of survival, chemotherapy does not appear to improve survival in younger patients with localized and regional disease.
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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".