Evaluation of the implementation of the Expanded Prostate Cancer Index Composite for Clinical Practice (EPIC-CP) across the province of Ontario.
Bibliographic record
Abstract
188 Background: EPIC-CP, a prostate cancer Patient-Reported Outcome Measure (PROM), was implemented across Ontario’s 14 Regional Cancer Centres (RCCs) using a phased roll-out approach. This approach enabled implementation and evaluation in enrolled centres, with the aim of using the results to improve implementation in both the enrolled regions and those preparing to implement. The objective was to analyze formative evaluation results and identify opportunities for improvement in implementation. Methods: Surveys were developed for patients and providers to capture attitudes and experiences regarding EPIC-CP. Data collection was initiated approximately three months post-implementation, with three RCCs evaluated concurrently in ‘waves’. Descriptive analyses were performed to summarize the survey data. Results: To date, nine RCC’s have completed the formative evaluation with 104 providers and 517 patients responding to the surveys. Providers reported challenges with data flow (44% always or mostly received EPIC-CP results in a timely manner) and with responding to specific symptoms (27% and 29% reported that they are very confident in treating/managing sexual function and emotional issues respectively). Although 54% of providers acknowledged that using EPIC-CP should be part of their role, 46% indicated that it has added little or no value to their practice. In contrast, 75% of patients reported that EPIC-CP made it easier to communicate symptoms to their care team, which suggests that patients find EPIC adds value. Patient comments identified gaps such as educational resources for symptom management and additional support when completing the PROM. Conclusions: Opportunities to improve the patient and provider experience with EPIC-CP include: developing educational resources, addressing issues with technology and data flow, and identifying factors that influence provider uptake. These results will be used to design resources and support to improve the process for implementing EPIC-CP in later waves.
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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 | 0.041 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".