Pixel or Paper? Validation of a Mobile Technology for Collecting Patient-Reported Outcomes in Rheumatoid Arthritis
Bibliographic record
Abstract
BACKGROUND: In the management of chronic disease, new models for telemonitoring of patients combined with the choice of electronic patient-reported outcomes (ePRO) are being encouraged, with a clear improvement of both patients' and parents' quality of life. An Italian study demonstrated that ePRO were welcome in patients with rheumatoid arthritis (RA), with excellent matching data. OBJECTIVE: The aim of this study is to evaluate the level of agreement between electronic and paper-and-pencil questionnaire responses. METHODS: This is an observational prospective study. Patients were randomly assigned to first complete the questionnaire by paper and pencil and then by tablet or in the opposite order. The questionnaire consisted of 3 independent self-assessment visual rating scales (Visual Analog Scale, Global Health score, Patient Global Assessment of Disease Activity) commonly used in different adult patients, including those with rheumatic diseases. RESULTS: A total of 185 consecutive RA patients were admitted to hospital and were enrolled and completed the questionnaire both on paper and on electronic versions. For all the evaluated items, the intrarater degree of agreement between 2 approaches was found to be excellent (intraclass correlation coefficient>0.75, P<.001). CONCLUSIONS: An electronic questionnaire is uploaded in a dedicated Web-based tool that could implement a telemonitoring system aimed at improving the follow-up of RA patients. High intrarater reliability between paper and electronic methods of data collection encourage the use of a new digital app with consequent benefit for the overall health care system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".