Association Between Colonic Screening, Subject Characteristics, and Stage of Colorectal Cancer
Bibliographic record
Abstract
OBJECTIVES: Colorectal cancer remains a significant cause of mortality and morbidity in North America. Colorectal cancer survival is highly dependent on stage at diagnosis, therefore it is important to identify factors related to stage. This study evaluated the association between subject factors (e.g., colonic screening, family history) and stage of colorectal cancer at diagnosis. METHODS: Population-based colorectal cancer cases recruited by the Ontario Familial Colon Cancer Registry between 1997 and 1999 were staged according to the tumor-nodal-metastasis (TNM) staging system and classified as early (TNM I/II) or late (TNM III/IV) stage. Epidemiologic information and stage was available for 768 cases. Multivariate logistic regression was used to obtain odds ratios (OR) estimates. RESULTS: Having had screening endoscopy reduced the risk of late stage diagnosis (OR = 0.46, 95% CI 0.22-0.98). Being older (>45 yr) was associated with a reduced risk of late stage cancer (OR = 0.36, 95% CI 0.18-0.74), as was having a first degree relative with colorectal cancer (OR =0.66, 95% CI 0.46-0.95). Rural residence (OR = 1.48, 95% CI 1.01-2.17) and non-white ethnicity (OR = 3.34, 95% CI 1.20-9.36) were associated with an increased risk of late stage cancer. CONCLUSIONS: Several factors are independently associated with late stage colorectal cancer. Colorectal cancer screening awareness and education programs need to consider targeting persons most likely to present with late stage colorectal cancer.
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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 | 0.004 |
| 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) | 0.003 | 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".