Toward Electronic Health Recording: Evaluation of Electronic Patient-reported Outcome Measures System for Remote Monitoring of Early Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To assess the use of electronic patient-reported outcome measures (ePROM) in standard clinical practice for early rheumatoid arthritis (RA) management, the ePROM ability to enhance clinical care, and how computing technology can improve the patients' adherence to therapy. METHODS: In a double-blinded randomized-controlled study, 211 patients with early RA diagnosed according to American College of Rheumatology/European League Against Rheumatism criteria completed a PROM in paper format at their first clinic visit. Patients were then randomized to Group 1, which completed an ePROM questionnaire monthly, or Group 2, which continued the standard paper PROM format. Over a 12-month period, Group 1 patients were assessed every 3 months in the clinic, whereas Group 2 patients were assessed in the clinic initially monthly for 6 months, then every 3 months. The primary endpoint was the equivalence of outcomes [Routine Assessment of Patient Index Data 3 (RAPID-3) and 28-joint Disease Activity Score (DAS28)] in both groups. The secondary endpoint was the patients' adherence to their medications. RESULTS: There was no significant difference between disease activity measures as well as DAS28 and RAPID-3 scores at 3, 6, and 12 months of management, although there was a trend toward lower patient-reported tender joint count and functional disability score in the active group versus the control group. The patients' adherence to antirheumatic therapy was significantly higher (p < 0.01) in the ePROM group, whereas stopping disease-modifying antirheumatic drugs for intolerability was significantly higher (p < 0.01) in the control group at 12 months of treatment. CONCLUSION: We found ePROM equivalent to standard paper PROM format. Further, it enabled the patients to personally monitor how they are doing regarding their disease activity and helped to optimize their adherence to their treatment.
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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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".