Systematic Review of Outcomes and Patient Experience With Virtual Care in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic review on patient outcomes of virtual care compared to conventional care in rheumatoid arthritis (RA), including disease activity and patient experience. METHODS: A systematic search of Medline, Embase, CINAHL, and the Cochrane Central Register of Controlled Trials was performed from database inception to March 19, 2020. Observational and randomized controlled trials (RCTs) describing the use of RA virtual care supplanting conventional visits and reporting on disease activity and/or patient experience were included. A narrative synthesis of results was conducted, as a meta-analysis was not possible due to heterogeneity of study designs and outcome reporting. RESULTS: A total of 352 studies were identified, and 6 were selected for final inclusion: 3 RCTs and 3 observational studies. Disease activity and patient experience were comparable between virtual and conventional care models. In addition, 1 RCT found no difference in observed outcomes between virtual care delivered by a rheumatologist and by a rheumatology nurse. Virtual care was found to have additional benefits for improved treatment adherence, maintenance of functional status, and quality of life. The overall risk of bias was low in 2 of 3 RCTs, but high in the observational studies. Study quality was limited by incomplete data reporting, lack of sample size justification, and sufficient timeframe to assess objectives. CONCLUSION: Limited evidence exists that virtual RA care is an acceptable alternative to conventional care, maintaining comparable patient outcomes and experience of care. Additional research into effective implementation strategies and long-term health system and patient outcomes of virtual care are needed.
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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 | 0.016 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".