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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".