A population-based study examining the influence of a specialized rapid-access cancer clinic on initial treatment choice in localized prostate cancer
Bibliographic record
Abstract
Introduction: Treatment decisions in localized prostate cancer are complicated by the available choices. A rapid-access cancer clinic (RAC) has been unique to Calgary, AB, since 2007. This RAC offers multidisciplinary prostate cancer education by a urologist, medical oncologist, and radiation oncologist. It is hypothesized that treatment utilization data from decisions taken at RAC may serve to benchmark the appropriateness of treatment decisions on a population level.Methods: Records of patients with clinically localized prostate cancer in Alberta between October 1, 2007 and September 30, 2009 were reviewed with ethics approval. Records were linked to the Alberta Cancer Registry database. Clinical, treatment, and health services characteristics pertaining to patients attending RAC were compared to the general population. The primary endpoint was utilization rates of each initial treatment.Results: During this two-year period, 2838 patients were diagnosed with localized prostate cancer; 375 attended RAC. The utilization rates among RAC patients vs. the whole Alberta population were: prostatectomy 60.3% (95% confidence interval [CI] 55.3–65.2) vs. 48.0% (95% CI 47.1‒50.7; χ2 p<0.001); active surveillance 16.0% (95% CI 12.3‒19.7%) vs. 13.5% (95% CI 12.2‒15.8; χ2 p=0.214); radiotherapy 11.7% (95% CI 8.5‒15.0) vs. 18.0% (95% CI 16.9‒20.5; χ2 p=0.002); and hormone therapy 8.0% (95% CI 5.2‒10.8) vs. 17.4% (95% CI 16.1‒18.9; χ2 p<0.001).Conclusions: A specialized clinic for localized prostate cancer may be associated with a higher likelihood of receiving surgery or active surveillance as initial treatment compared to the prostate cancer population in Alberta.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".