Adaptation of American College of Rheumatology Rheumatoid Arthritis Disease Activity and Functional Status Measures for Telehealth Visits
Bibliographic record
Abstract
OBJECTIVE: To provide guidance on the implementation of recommended American College of Rheumatology (ACR) rheumatoid arthritis (RA) disease activity and functional status assessment measures in telehealth settings. METHODS: An expert panel was assembled from the recently convened ACR RA disease activity and functional status measures working groups to summarize strategies for implementation of ACR-recommended RA disease activity (the Clinical Disease Activity Index [CDAI], Disease Activity Score in 28 joints using the erythrocyte sedimentation rate or the C-reactive protein level [DAS28-ESR/CRP], Patient Activity Scale II [PAS-II], Simplified Disease Activity Index [SDAI], and Routine Assessment of Patient Index Data 3 [RAPID3]) and functional status (the Health Assessment Questionnaire II [HAQ-II], Multidimensional Health Assessment Questionnaire [MDHAQ], and PROMIS physical function 10-item short form [PROMIS PF-10]) measures in telehealth settings. RESULTS: Measures composed of patient-reported items (disease activity: PAS-II, RAPID3; functional status: HAQ-II, MDHAQ, PROMIS PF-10) require minimal modification for use in telehealth settings. Measures requiring formal joint counts (the CDAI, DAS28-ESR/CRP, and SDAI) can be calculated using patient-reported swollen and tender joint counts. When the feasibility of laboratory testing is limited, the CDAI can be used in place of the SDAI, and scoring modifications of the DAS28-ESR/CRP without the acute-phase reactant are available. Assessment of the validity of these modifications is limited. Implementation of these measures can be facilitated by electronic health record collection, mobile applications, and provider/staff administration during telehealth visits. CONCLUSION: The ACR-recommended RA disease activity and functional status measures can be adapted for use in telehealth settings to support high-quality clinical care. Research is needed to better understand how telehealth settings may impact the validity of these measures.
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 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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".