A comparison of systemic breast cancer therapy utilization in Canada (British Columbia), Scotland (Dundee), and Australia (Western Australia) with models of “optimal” therapy
Bibliographic record
Abstract
BACKGROUND: Different jurisdictions report different breast cancer treatment rates. Evidence-based optimal utilization models may be specific to the derived population. We compared predicted optimal with actual endocrine and chemotherapy utilization in British Columbia, Canada; Dundee, Scotland; and Perth, Western Australia. DESIGN: Data were analyzed for differences in demography, tumour, and treatment. Epidemiological data were fitted to published Australian optimal radiotherapy utilization trees and region-specific optimal treatment rates were calculated. Optimal and actual systemic therapy rates from 2 population-based and 1 institution-based cancer registries were compared for patients diagnosed with breast cancer between 2000-2004, and 2002 for British Columbia. RESULTS: Chemotherapy rates differed between British Columbia (32%), Perth (29%), and Dundee (24%, p = 0.014). Endocrine therapy rates were similar between British Columbia (56%), Perth (59%), and Dundee (64%, p > 0.05). Actual utilization rates were lower than optimal estimates for chemotherapy, but higher for endocrine therapy. Region-specific optimal utilization rates at diagnosis varied between 50-56% for chemotherapy, and 49-54% for endocrine therapy. Variation was attributed to local differences in demographics, and tumour stage. CONCLUSION: Actual treatment rates varied. There was lower than estimated optimal chemotherapy use but higher than expected use of endocrine therapy.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".