A comparison of surgical and radiotherapy 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 utilization models may be specific to derived populations. We compared predicted optimal with actual radiotherapy utilization in British Columbia, Canada; Dundee, Scotland; and Perth, Western Australia. DESIGN: Data were analyzed for differences in demography, tumor, and treatment. Epidemiological data were fitted to published Australian optimal radiotherapy utilization trees and region-specific optimal treatment rates were calculated. Optimal and actual surgery/radiotherapy rates from 2 population-based and 1 institution-based registries were compared for patients diagnosed with breast cancer between 2000 and 2004, and 2002 for British Columbia. RESULTS: Mastectomy rates differed between British Columbia (40%), Western Australia (44%), and Dundee (47%, p<0.01). Radiotherapy rates differed between British Columbia (60%), Western Australia (52%), and Dundee (49%, p<0.01). Actual radiotherapy utilization rates were lower than optimal estimates. Region-specific optimal utilization rates at diagnosis varied from 57% to 71% for radiotherapy and 62% to 64% when taking into account patient preference. Variation was attributed to local differences in demography and tumor stage. CONCLUSIONS: Actual treatment rates varied, and were associated with patterns of care and guideline differences. Actual radiotherapy rates were lower than optimal rates. Differences between optimal and actual utilization may be due to access shortfalls, and patient preference.
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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 | 0.001 |
| 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 | 0.000 |
| 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".