Clinical Visit Frequencies in Rheumatology: A Systematic Literature Review
Bibliographic record
Abstract
OBJECTIVE: Clinical visits are a fundamental aspect of rheumatic disease care, but recommendations for appropriate visit frequencies are largely absent from guidelines, scarcely studied, and inconsistently reported. The aim of this systematic review was to summarize the evidence pertaining to visit frequencies for major rheumatic diseases. METHODS: This systematic review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Title/abstract screening, full-text screening, and extraction were carried out by 2 independent authors. Annual visit frequencies were either extracted or calculated and stratified by disease type and country of study. Weighted mean annual visit frequencies were calculated. RESULTS: A total of 273 relevant manuscript records were screened, and 28 were included after applying selection criteria. The included studies were equally divided between US and non-US and were published between 1985 and 2021. Most (n = 16) focused on rheumatoid arthritis (RA), systemic lupus erythematosus (SLE; n = 5), and fibromyalgia (FM; n = 4). For RA, the average annual visit frequencies were 5.25 for US rheumatologists, 4.80 for US non-rheumatologists, 3.29 for non-US rheumatologists, and 2.74 for non-US non-rheumatologists. For SLE, annual visit frequencies for non-rheumatologists were much higher than for US rheumatologists (12.3 versus 3.24). For FM, annual visit frequencies were 1.80 for US rheumatologists and 0.40 for non-US rheumatologists. There was a decreasing trend of visit frequency to rheumatologists from 1982 to 2019. CONCLUSION: Evidence for rheumatology clinical visits was limited and heterogeneous on a global scale. However, general trends suggest more frequent visits in the US and less frequent visits in recent years.
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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 | 0.015 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".