Abstract P3-11-11: A Comparison of Breast Cancer Treatment Rates in British Columbia, Scotland, and Western Australia, and a Comparison with Models of “Optimal” Therapy
Bibliographic record
Abstract
Abstract Background: Evidence-based optimal utilization models provide estimates of optimal radiotherapy, chemotherapy and hormonal therapy utilization by stage and other clinically relevant patient sub-groups. We therefore compared predicted utilization with actual utilization of radiotherapy, hormone therapy and chemotherapy in 3 jurisdictions with population-based stage and treatment data: British Columbia, Canada; Dundee, Scotland; and Perth, Western Australia. Methods: Previously published optimal radiotherapy, chemotherapy, and endocrine therapy treatment utilization trees for an Australian population were modified to incorporate epidemiological data from British Columbia, Dundee, and Perth, such that the optimal trees for each region reflected the casemix for each region. Frequency data on patient, tumour, and surgical factors were used to calculate optimal treatment rates for each region. Optimal rates were then compared with actual rates of surgery, radiotherapy, chemotherapy, and endocrine therapy use obtained from 2 population-based and 1 institution-based cancer registries for patients diagnosed with breast cancer between 2000 to 2004. Information on region-specific treatment guidelines was also collected. Results: Region-specific optimal treatment utilization rates at diagnosis varied between 80% and 81% for radiotherapy (62 to 64% when patient preference is taken into account), 53% to 56% for chemotherapy, and 49% to 54% for endocrine therapy. The predicted ranges were due to local variations in demographics, and tumour stage. Actual radiotherapy utilization was 57%, 49%, and 52%; chemotherapy utilization was 32%, 24%, and 29%; and endocrine therapy utilization was 56%, 64%, and 52% for British Columbia, Dundee, and Perth, respectively. Conclusion: There are significant differences in actual treatment utilisation rates between the study populations. It is unlikely that all of this variation is due to differences in tumour characteristics alone. Actual utilization rates were lower than the calculated optimal rates for radiotherapy and chemotherapy, and higher for endocrine therapy. Differences between actual regional rates of treatment utilization were seen, and were associated with differences in mastectomy rates, and guideline recommendations for treatment use in that region. This methodology allows comparison of the treatment that occurs in a jurisdiction against what would be considered optimal based on evidence. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P3-11-11.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".