Factors associated with screen-detected breast cancer across five Canadian provinces: a CanIMPACT study
Bibliographic record
Abstract
IntroductionBreast cancer screening is intended to identify cancer in early stages when prognosis is better and treatments less invasive. Objectives and ApproachWe describe Canadian inter- and intra-provincial variation in the percentage of screen-detected cases and identify factors related to having a screen-detected versus a non-screen detected breast cancer. Breast cancers diagnosed from 2004/7 to 2010/11/12 in 5 Canadian provinces were included. Standard provincial datasets were created using screening program and claims data. A common algorithm (Alberta, Ontario) or variable from the screening dataset (British Columbia, Manitoba, Nova Scotia) was used to identify the mode of diagnosis (screening versus not). Relationship between screen-detected cancer and several demographic, clinical and healthcare utilization factors were explored. ResultsThe percentage of screen-detected breast cancers varied from 25 to 40 percent across provinces; it ranged 43 to 51 percent for those aged 50-69. Within provinces, the percentage of screen-detected cancers varied across regional health authorities by a low of 1\% to a high of 33\%. Urban residence was positively associated with screen-detection in some provinces and negatively in others. Women in the lowest neighborhood income quintile had the smallest proportion of screen-detected cancers; the absolute difference from those in the highest quintiles ranged from 3.3-11.5\% across provinces. High continuity of care with a usual primary care provider was positively associated with having a screen-detected cancer compared to those with no usual care provider. Conclusion/ImplicationsThe proportion of screen-detected breast cancers varied significantly across and within provinces suggesting geographic variability in access to screening services. Variation across provinces in terms of factors associated with screen-detected breast cancer also likely reflect access issues. The positive association of high continuity of care with screen-detection in all provinces suggests that regular care with a primary care physician is an important factor in improving screening rates and detection.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".