Breast Screening Outcomes in Women with and without a Family History of Breast and/or Ovarian Cancer
Bibliographic record
Abstract
OBJECTIVES: To compare breast screening outcomes between women with a moderate or strong family history of breast and/or ovarian cancer with those without such a history. SETTING: The Ontario Breast Screening Programme (OBSP) is a population-based programme offering mammography and clinical breast examination to Ontario women of 50 and older. METHODS: Data from a cohort of 143,574 women screened by the OBSP from 1996 to 1997 were included. Referral rates, cancer detection rates, positive predictive values and the histological features of screen-detected cancers were examined within family history groups, age groups and screening modalities. Logistic regression analysis of cancer detection was conducted to adjust for potential confounding variables; subgroup analysis by hormone replacement therapy (HRT) use was also undertaken. RESULTS: Compared with women with no family history, women with a moderate or strong family history of breast and/or ovarian cancer were more likely to have their cancer detected (odds ratio [OR]=1.44, 95% confidence interval [CI] 1.20-1.74 and OR=1.42, 95% CI 1.10-1.83, respectively). Among women using HRT, however, there was no association observed between family history and cancer detection (moderate: OR=0.98, 95% CI 0.65-1.48; strong: OR=1.17, 95% CI 0.68-2.02) with history. The histological features of invasive tumours were similar among family history groups. CONCLUSIONS: Greater cancer detection rates and high proportions of invasive tumours with good prognosis indicate that women aged 50 and over with a family history may have the potential to benefit from regular breast cancer screening. Further studies are required to identify optimal screening guidelines and to examine whether HRT reduces the ability to detect cancer in these women.
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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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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".