Wait Times for Rheumatology Consultation
Bibliographic record
Abstract
OBJECTIVES: Patients with rheumatoid arthritis (RA) should be seen by a rheumatologist promptly; however, there are no recommendations for patients with osteoarthritis (OA). Our goal was to describe wait times from referral by the primary care provider to rheumatology consultation and to explore whether wait times are associated with type of arthritis diagnosis, geographic area, or type of rheumatology office. METHODS: Appointments were requested by telephone using case scenarios that were created by a group of experts and included (1) presumed RA, (2) possible RA, and (3) presumed OA. Wait times were evaluated as the time between the initial request and the appointment date provided. We used descriptive statistics, bivariate analysis, and logistic regression in the analysis. RESULTS: For all scenarios combined, 34% were given an appointment with a rheumatologist within 3 months of referral, 32% waited longer than 3 months, and 34% were told that the rheumatologist was not accepting new referrals at the time the request was made. Patients with presumed RA were much more likely to be seen within 3 months of referral compared with those with presumed OA (odds ratio, 13; 95% confidence interval, 1.70-99.38). CONCLUSIONS: Rheumatoid arthritis is prioritized over OA for rheumatology appointments. However, most patients with RA are still not receiving an appointment to a rheumatologist in a timely manner. Effective triage tools to decrease these delays should be instituted.
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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 | 0.001 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".