An audit of referral and treatment patterns of high-risk prostate cancer patients in Alberta
Bibliographic record
Abstract
INTRODUCTION: We aimed to determine the impact of clinical practice guidelines (CPG) on rates of radiation oncologist (RO) referral, androgen-deprivation therapy (ADT), radiation therapy (RT), and radical prostatectomy (RP) in patients with high-risk prostate cancer (HR-PCa). METHODS: All men >18 years, diagnosed with PCa in 2005 and 2012 were identified from the Alberta Cancer Registry. Patient age, aggregated clinical risk group (ACRG) score, Gleason score (GS), pre-treatment prostate-specific antigen (PSA), RO referral, and treatment received were extracted from electronic medical records. Logistic regression modelling was used to examine associations between RO referral rates and relevant factors. RESULTS: HR-PCa was diagnosed in 261 of 1792 patients in 2005 and 435 of 2148 in 2012. Median age and ACRG scores were similar in both years (p>0.05). The rate of patients with PSA >20 were 67% and 57% in 2005 and 2012, respectively (p=0.004). GS ≤6 was found in 13% vs. 5% of patients, GS 7 in 27% vs. 24%, and GS ≥8 in 59% vs. 71% in 2005 and 2012, respectively (p<0.001). In 2005, RO referral rate was 68% compared to 56% in 2012 (p=0.001), use of RT + ADT was 53% compared to 32% (p<0.001), and RP rate was 9% vs. 17% (p=0.002). On regression analysis, older age, 2012 year of diagnosis and higher PSA were associated with decreased RO referral rates (odds ratios [OR] 0.49, 95% confidence interval [CI] 0.39-0.61; OR 0.51, 95% CI 0.34-0.76; and OR 0.64, 95% CI 0.39-0.61), respectively [p<0.001]). CONCLUSIONS: Since CPG creation in 2005, RO referral rates and ADT + RT use declined and RP rates increased, which demonstrates a need to improve adherence to CPG in the HR-PCa population.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".