Data from Gravida and Birth Outcomes Prior to and after Diagnosis of Early Age–Onset Colorectal Cancer among Female Patients: Population-Based Epidemiologic Studies
Bibliographic record
Abstract
AbstractBackground: Early age–onset colorectal cancer (EAO-CRC) strikes during the reproductive years, yet pregnancies before and after diagnosis have not been thoroughly studied. Our objective was to comprehensively examine: (i) the relationship between gravida and EAO-CRC and (ii) the relationship between EAO-CRC and births after cancer diagnosis. Methods: We conducted a case–control and a cohort study using administrative health data from British Columbia, Canada, of females diagnosed with EAO-CRC from 2005 to 2017 and age- and sex-matched cancer-free controls. Multivariable logistic regression models were used to evaluate: (i) the association between gravida assessed over the 5-year prodrome period before cancer diagnosis and EAO-CRC and (ii) the association between EAO-CRC and births assessed over a 5-year period following cancer diagnosis. Results: The study sample consisted of 865 females (age at EAO-CRC diagnosis 42.5 ± 6.1 years) with EAO-CRC and 8,291 controls (42.4 ± 6.3 years). Females with a gravida of ≥2 in the 5-year prodrome period had 1.82 times the odds of EAO-CRC compared with those with gravida of 0 (OR, 1.82; 95% confidence interval, 1.19–2.78). After cancer diagnosis, females with EAO-CRC had significantly lower odds of giving birth within five years (OR, 0.23; 95% confidence interval, 0.15–0.37). Older age, lower income, rural residence, and greater healthcare utilization were associated with lower odds of post-diagnosis births. Conclusions: Our study highlights the complex relationship between reproductive health and EAO-CRC. Impact: Findings indicate a need for comprehensive psychosocial support addressing family planning for female patients with EAO-CRC.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".