Establishing an electronic patient-reported outcome (ePRO) for patients with endometriosis and chronic pelvic pain: A pilot feasibility study
Bibliographic record
Abstract
Abstract Introduction: Endometriosis and chronic pelvic pain (CPP) are complex conditions that significantly impact quality of life. Few tools systematically capture patient-reported outcomes in this population. This pilot study evaluated patients’ experiences and the perceived usability of an electronic Patient-Reported Outcome (ePRO) tool to assess its feasibility in supporting a clinical data registry. Associations between demographic/clinical characteristics and ePRO usability were also explored. Methods: This prospective observational study included patients enrolled at a tertiary endometriosis and CPP clinic who completed a REDCap-based ePRO survey remotely. The survey included demographic items and 13 validated instruments assessing pain, psychological distress, sensory processing, and quality of life. Usability was evaluated through an Online Questionnaire-Experiences Survey (OQES), covering accessibility, completion experience, redundancy, and content relevance. Descriptive statistics, t-tests, and Hedges’ g were used for analysis; open-ended responses were thematically reviewed. Results: Fourteen patients were invited; 11 (78.6%) completed the full ePRO. Most found it easy to access (90.9%) with stable internet (100%). While 63.6% reported some redundancy, none reported discomfort, and 90.9% agreed the survey captured relevant experiences. Participants with higher Central Sensitization Inventory (CSI) and Generalized Anxiety Disorder-7 (GAD-7) scores were more likely to complete all items ( P = 0.042 and .047). Those who did not perceive redundancy scored significantly higher on the Pain Catastrophizing Scale ( P = .048) and Endometriosis Health Profile-30 ( P = .016). Conclusion: The ePRO tool showed high feasibility. Patients with higher symptom burden were more likely to find it useful. Future improvements should reduce redundancy and clarify survey instructions.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".