Family history of hormonal cancers and colorectal cancer risk: A case‐control study conducted in Ontario
Bibliographic record
Abstract
Aggregation of cancers among families with highly penetrant genetic mutations such as hereditary nonpolyposis colorectal cancer is well-described. However, there is a paucity of data regarding familial aggregation of hormonal cancers (cancers of the breast, endometrial, ovarian and prostate) and colorectal cancer (CRC) in the general population. We investigated the association between having a first-degree family history of breast, endometrial, ovarian, or prostate cancer and CRC risk. Population-based CRC cases and controls were recruited by the Ontario Familial Colorectal Cancer Registry (OFCCR). Logistic regression was conducted to obtain odds ratio (OR) estimates and 95% confidence intervals (95% CIs). First-degree family history of breast cancer was associated with a modest, borderline statistically significant increased CRC risk (age-, sex-adjusted OR = 1.2, 95% CI = 1.0, 1.5). The magnitude of CRC risk was greatest if more than one first-degree kin had breast cancer (age-, sex-adjusted OR = 1.7, 95% CI = 1.0, 2.0), as well as if the kin was diagnosed at >50 years of age (age-, sex-adjusted OR = 1.4, 95% CI = 1.1, 1.8). Family history of ovarian cancer was associated with reduced CRC risk (multivariate-adjusted OR = 0.6, 95% CI = 0.3, 1.0). Although statistically significant increases in CRC risk were observed in the age-, sex-adjusted OR estimates for family history of endometrial and prostate cancers, the associations were no longer significant after multivariate-adjustment. In conclusion, individuals with a first-degree kin with breast cancer may have a modest increased risk for CRC compared to individuals without.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".