Healthcare and Cancer Treatment Costs of Breast Screening Outcomes among Higher than Average Risk Women
Bibliographic record
Abstract
Concurrent cohorts of 644,932 women aged 50-74 screened annually due to family history, dense breasts or biennially in the Ontario Breast Screening Program (OBSP) from 2011-2014 were linked to provincial administrative datasets to determine health system resource utilization and costs. Age-adjusted mean and median total healthcare costs (2018 CAD) and incremental cost differences were calculated by screening outcome and compared by recommendation using regression models. Healthcare costs were compared overall and 1 year after a false positive (n = 46,081) screening mammogram and 2 years after a breast cancer diagnosis (n = 6011). Mean overall healthcare costs by age were highest for those 60-74, particularly with annual screening for family/personal history (CAD 5425; 95% CI: 5308 to 5557) compared to biennial. Although the mean incremental cost difference was higher (23.4%) by CAD 10,235 (95% CI: 6141 to 14,329) per breast cancer for women screened annually for density ≥ 75% compared to biennially, the cost difference was 12.0% lower (-CAD 461; 95% CI: -777 to -114) per false positive result. In contrast, for women screened annually for family/personal history, the mean cost difference per false positive was 19.7% higher than for biennially (CAD 758; 95% CI: 404 to 1118); however, the cost difference per breast cancer was only slightly higher (2.5%) by CAD 1093 (95% CI: -1337 to CAD 3760). Understanding that associated costs of annual compared to biennial screening may balance out by age and outcome can assist decision-making regarding the use of limited healthcare resources.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".