Predictors of CRC Stage at Diagnosis among Male and Female Adults Participating in a Prospective Cohort Study: Findings from Alberta’s Tomorrow Project
Bibliographic record
Abstract
Colorectal cancer (CRC) is a leading cause of morbidity and mortality in Canada. CRC screening and other factors associated with early-stage disease can improve CRC treatment efficacy and survival. This study examined factors associated with CRC stage at diagnosis among male and female adults using data from a large prospective cohort study in Alberta, Canada. Baseline data were obtained from healthy adults aged 35-69 years participating in Alberta's Tomorrow Project. Factors associated with CRC stage at diagnosis were evaluated using Partial Proportional Odds models. Analyses were stratified to examine sex-specific associations. A total of 267 participants (128 males and 139 females) developed CRC over the study period. Among participants, 43.0% of males and 43.2% of females were diagnosed with late-stage CRC. Social support, having children, and caffeine intake were predictors of CRC stage at diagnosis among males, while family history of CRC, pregnancy, hysterectomy, menopausal hormone therapy, lifetime number of Pap tests, and household physical activity were predictive of CRC stage at diagnosis among females. These findings highlight the importance of sex differences in susceptibility to advanced CRC diagnosis and can help inform targets for cancer prevention programs to effectively reduce advanced CRC and thus improve survival.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".