Does young age influence the prognosis of colorectal cancer: a population-based analysis
Bibliographic record
Abstract
BACKGROUND: Controversy exists whether young patients diagnosed with colorectal cancer have a poorer prognosis. Although younger patients are more likely to have certain poor prognostic factors, prior studies have shown mixed results in terms of overall prognosis, which may be due to lack of adjustment for confounding factors. The primary objective of our study was to determine the effect of age on survival following treatment of colorectal cancer in the Province of Manitoba, Canada, while controlling for important cofactors. METHODS: This was a population-based analysis of all adult patients (age≥18 years) diagnosed with adenocarcinoma of the colon or rectum between 1 January 2004 and 31 December 2006 in the Province of Manitoba. Patient, tumor, and treatment factors were identified using administrative data. Five-year Kaplan-Meier survival and Cox proportional hazards model were analyzed to determine whether young age (45 years of age or younger) was associated with a poorer prognosis, while controlling for confounding variables. RESULTS: Of the 2,086 patients identified, 70 (3.36%) were considered young. These patients were more likely to have T4 tumors and node-positive disease. Older patients had more advanced comorbidities. Young age was an independent predictor of better survival. Poorer survival was associated with male gender, increasing stage, higher grade, comorbidity, lower socioeconomic status, and lack of receipt of surgery or chemotherapy. CONCLUSIONS: Young people make up a small minority of patients with colorectal cancer. Young patients present with more locally advanced colorectal cancer. Despite this, on a population basis, their prognosis may be more favorable than their older counterparts when controlling for disease, patient, and treatment factors.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".