Stratification of 5-year cancer detection rate in an organized breast screening program based on Gail model risk factors.
Bibliographic record
Abstract
7 Background: The Gail model has been validated in the United States and several European countries, but to our knowledge, it has not been validated in organized breast screening programs in Canada. The Screening Mammography Program of British Columbia (SMPBC) records participant data from a questionnaire based on Gail model parameters (which include family and personal medical history). This study investigates whether the Gail model is a valid tool to predict the breast cancer risk for the population undergoing screening mammography in the province of BC. Methods: Client information of the 223,349 British Columbian women who participated in the year 2000, along with their tumor status from 2000-2004, was extracted from the provincial database. A software program was developed to rapidly calculate the absolute 5-year Gail score from questionnaire data. Participant data was separated into .5% risk intervals and also into quintiles based on increasing Gail scores, and the mean absolute risks were compared to the actual five year rate of cancer as detected by the SMPBC. Results: Overall, goodness of fit between Gail score and SMPBC detection (E/O) across the categories can be rejected (χ2=247.9, df=9, p value < .001). The Gail model significantly underpredicts the cancer detection for risk categories up to 2%, however it provides a sufficient fit for categories 2%-4% as the E/O ratio is not significantly different from 1.0 in these intervals. For the highest risk interval, categorized as greater than 4% risk, the model significantly overpredicts cancer detection. Additionally, when presented in quintiles, the Gail model under-predicts risk in all but the highest quintile (1.77-11.43% risk range). Conclusions: Our results, based on participants of SMPBC, suggest that the Gail model significantly under-predicts cancer detection. Although this model provides a sufficient fit for women with a Gail score between 1.51-4%, it does not predict breast cancer risk accurately for low and high risk women in the Screening Mammography Program of BC.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".