Breast cancer detection method, diagnostic interval and use ofspecialized diagnostic assessment units across Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: Breast cancer is detected through screening or through signs and symptoms. In Canada, mammograms for breast cancer screening are offered in organized programs or independently (opportunistic screening). Province of Ontario breast Diagnostic Assessment Units (DAUs) are facility-based programs that provide coordinated breast cancer diagnostic services, as opposed to usual care, in which the primary care provider arranges the tests and consultations. This study describes breast cancer detection method, diagnostic interval and DAU use across Ontario. METHODS: The study cohort consisted of 6898 women with invasive breast cancer diagnosed in 2011. We used the Ontario Cancer Registry linked to administrative health care databases. We determined the detection method using the Ontario Breast Screening Program (OBSP) data and physician claims. The diagnostic interval was the time between the initial screen, specialist referral or first diagnostic test and the cancer diagnosis. The diagnostic route (whether through DAU or usual care) was determined based on the OBSP records and biopsy or surgery location. We mapped the diagnostic interval and DAU coverage geographically by women's residence. RESULTS: In 2011, 36% of Ontario breast cancer patients were screen-detected, with a 48% rate among those aged 50 to 69. The provincial median diagnostic interval was 32 days, with county medians ranging from 15 to 65 days. Provincially, 48.4% were diagnosed at a DAU, and this ranged from zero to 100% across counties. CONCLUSION: The screening detection rate in age-eligible breast cancer patients was lower than published population-wide screening rates. Geographic mapping of the diagnostic interval and DAU use reveals regional variations in cancer diagnostic care that need to be addressed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".