Evaluation of the Rheumatoid Arthritis Impact of Disease (RAID) Score in Assessing Rheumatoid Arthritis Activity in Teleconsultation
Bibliographic record
Abstract
OBJECTIVE: To evaluate the relevance of the Rheumatoid Arthritis Impact of Disease (RAID) score as a disease activity marker of rheumatoid arthritis (RA) in a teleconsultation setting. METHODS: A prospective, observational, 24-month, single-center study involving patients with RA who underwent teleconsultations was performed. The RAID score was sent to all patients by email and completed the day before the scheduled session. The RAID questionnaire was also completed just prior to the next scheduled face-to-face consultation. The same physician performed teleconsultation/in-person consultations and was unaware of the RAID results. RESULTS: < 0.001). A RAID score > 2 was associated with the best combination of sensitivity (94%) and specificity (43%) for the indication of rapid in-person consultation because of insufficiently controlled disease activity, with an area under the curve of 0.74. All 23 patients with RAID < 2 had no intercurrent flares; overall physician global assessment was 1.6 of 10 (SD 1.4), DAS28-CRP 1.5 (SD 0.2), and CRP 1.8 (SD 1.4) mg/L. CONCLUSION: Our findings reinforce the RAID score as a valuable tool in teleconsultation, exhibiting a good correlation with disease activity variables. Using a RAID score threshold of 2 during teleconsultations could distinguish patients with good disease control and those with the potential need for an in-person visit.
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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 | 0.003 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".