Evaluation of the Expanded Prostate Cancer Index Composite for Clinical Practice (EPIC-CP) tool: Acceptability, feasibility and potential role in enhancing clinical care of men with early-stage prostate cancer.
Bibliographic record
Abstract
e21631 Background: The purpose of this multi-site study was to test feasibility of implementing the Expanded Prostate Cancer Index Composite for Clinical Practice (EPIC-CP) symptom tool in routine ambulatory care and evaluate its acceptability and role in customizing care from the perspective of patients and clinicians. Methods: This feasibility study recruited prostate cancer patients from four cancer centres between November 2014 and June 2015. Eligible patients were those attending radiation or surgical oncology consultation, follow-up, or on-treatment review. Patient participants completed the EPIC-CP symptom reporting tool, results of which were reviewed as part of the clinical encounter with nurse and/or physician. Experience with the tool was evaluated from the patient perspective through a 9-item Patient Exit Survey; and from the provider perspective, through semi-structured qualitative interviews. Results from the patient and provider perspectives were analyzed and compared to identify common themes. Results: A total of 287 Patient Exit Surveys were completed. Patients averaged 2.8 EPIC-CP screens during the study. Missing data across all 16 items ranging from 0.5% (bowel function) to 9.5% (orgasm quality). Eighty-two percent (82%) of patients were willing to complete similar questionnaires at future clinic visits. Only a few patients (3.5%) felt that the EPIC-CP tool did not help with their clinical encounter, and only 4% felt that the content should not include questions about sexual functioning. Thematic analysis from provider interviews revealed that the EPIC-CP tool captures essential prostate-specific effects that facilitated person-centered communication and customization of interventions. Conclusions: EPIC-CP is highly endorsed by healthcare practitioners and by prostate patients across consultation and follow-up visits, and across four diverse cancer centres. The EPIC-CP tool captures prostate-specific symptom information that assists in enhancing clinical care and symptom management. Provincial roll-out of this tool as a standard of care is recommended.
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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 | 0.031 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".