Two-year review of the electronic Patient Reported Outcome (ePRO) system in the Prostate Clinic at the Vancouver Prostate Centre (VPC).
Bibliographic record
Abstract
107 Background: The Vancouver Prostate Centre (VPC) implemented an electronic patient reported outcomes (ePRO) system in April 2017 to collect data on a research protocol in collaboration with University Health Network of Toronto, funded by Prostate Cancer Canada. Responses to ePRO questionnaires collected on iPads were entered into the national PC360 registry initially and a global TrueNorth registry thereafter funded by Movember Foundation. Herein we describe our experience implementing the ePRO system over the last two years. Methods: All patients (pts) with a new diagnosis of localized prostate cancer, either before or just after therapy, were identified using the electronic medical record and were consented in person in clinic. After signing consent, pts were asked to complete ePROs, including EPIC-26, EQ-5D-5L, WHODAS 2.0, and Distress Thermometer, on iPads at designated clinic appointment. Results: Between April 3, 2017-March 31, 2019, 1027 pts were identified as being eligible to complete ePRO: 878 pts (85.5%) consented and completed ePRO at least once. In Year 1 (Apr 2017-Mar 2018), 76.7% of the 1075 planned ePRO questionnaires were completed vs 75% completed of 1258 planned in year 2. Most pts (82.3%,723/878) agreed to be contacted for future research. Of those, 424/723 were recruited to at least one additional registry study. Data for consented pts was missing for 155/878 (17.7%) each year, usually due to difficulty locating the pt to give him the iPad before he left the clinic. In a small percentage of cases, ePRO reports were incomplete due to fluctuating WiFi connectivity, inadequate time to complete the questions before the clinic appointment, refusal, or no show. Conclusions: ePROs for research were successfully implemented into clinical practice in the PCSC program as evidenced by high completion and low refusal rates but required significant manpower due to ethics board requirements. Challenges encountered included: training clinic staff and coordinators to direct pts to the iPad system to minimize missing ePRO assessments, technical difficulties with WiFi connectivity, requirement of ethics board mandating that staff interact with each pt individually to ensure the ePRO assessments are completed. The next step is to incorporate ePROs into clinical practice, in which case consent will not be required.
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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 | 0.030 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".