Edmonton Prostate Interdisciplinary Cancer Clinic (EPICC): Real-world efficacy outcomes of a multidisciplinary clinic for metastatic castration-resistant prostate cancer.
Bibliographic record
Abstract
36 Background: Multidisciplinary management improves complex treatment decision making in cancer care but its impact for metastatic castration resistant prostate cancer (M1 CRPC) has not been documented. The Edmonton Prostate Interdisciplinary Cancer Clinic (EPICC) is a multidisciplinary specialized clinic focused on the delivery of novel therapeutics (Androgen Receptor Axis Therapy; ARAT) to men with chemotherapy-naïve M1 CRPC. The objective of the current study was to assess the efficacy of ARAT in the EPICC. Methods: The study was a retrospective quality assurance analysis. Eligible patients had a new diagnosis of chemotherapy-naïve M1 CRPC with minimal symptoms. EPICC patients were assessed and treated by a multidisciplinary cancer control team that included nursing oncology, pharmacy oncology and physician oncology (urologic, medical and radiation). Patients were treated in first line with an ARAT (abiraterone (AA) or enzalutamide (EZ)) from October 2017 to March 2018. The main efficacy outcome was overall survival (OS). The Kaplan-Meier method and Cox regression model were used to analyze survival data. Statistical tests were two-sided (p≤0.05). Results: From October 2017 to March 2018, 160 chemotherapy-naïve M1 CRPC patients were assessed in the EPICC. Median age at EPICC admission was 77 years (range, 54-92 years). Median PSA level at EPICC admission was 26.6 ng/mL (range, 0.1-5000 ng/mL). 84 out of 160 (53%) patients had received prior radical local therapy (RLT) with curative intent. 83 (57%) patients were treated with EZ and 64 (43%) patients were treated AA. Median OS for the entire cohort was 23 months. In multivariable analysis, absence of prior RLT (HR 3.6, 95% CI 1.9 to 6.6, p < 0.001), PSA > 20 ng/mL (HR 3.2, 95% CI 1.4 to 7.2, p = 0.004), and higher ECOG performance status (1 vs 0: HR 2.4, 95% CI 1.3 to 4.4, p = 0.005; 2 versus 0: HR 3.5, 95% CI 1.5 to 8.0, p = 0.003; and 3 versus 0: HR 12.7, 95% CI 2.5 to 63.8, p = 0.002) were independently associated with poorer OS. Conclusions: Multidisciplinary management of chemotherapy-naïve M1 CRPC with ARAT is feasible. Real world efficacy of ARAT in EPICC are similar to data reported in phase 3 trials.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".