Validation of the International Breast Cancer Intervention Study (IBIS) model in the High Risk Ontario Breast Screening Program: A retrospective cohort study
Bibliographic record
Abstract
PURPOSE: Women with a remaining lifetime risk of breast cancer of ≥25%, estimated using the International Breast Cancer Intervention Study (IBIS) model, were eligible for the High Risk Ontario Breast Screening Program. This study examined the performance of IBIS 10-year risk estimates in the program. METHODS: This retrospective study included 7487 women aged 30 to 69 years referred to the High Risk Ontario Breast Screening Program between July 1, 2011, and December 31, 2016, with follow-up until December 31, 2018. Model calibration and discrimination were assessed. Analyses were conducted overall and stratified by age (< or ≥50 years). Different 10-year risk thresholds were compared with the current eligibility criteria. RESULTS: Overall, IBIS overestimated the risk of breast cancer with an expected vs observed case ratio of 1.17 (95% CI = 1.04-1.35). Overestimation was highest in women aged 50 to 69 years (expected vs observed case ratio = 1.29, 95% CI = 1.03-1.69) and for those in the top quartile of risk. Overall discrimination was fair with a concordance statistic of 0.66 (95% CI = 0.63-0.70). Furthermore, when using different 10-year risk eligibility thresholds, most cases would have been missed in the 30 to 49 age group using the 8% 10-year risk threshold, whereas relatively few women aged 50 to 69 would have been ineligible at any of the thresholds examined. CONCLUSION: We found that IBIS overestimated the risk of breast cancer in this screening cohort but had adequate discrimination. Age-specific risk thresholds should be considered to optimize the program eligibility criteria.
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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 | 0.022 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".