Patterns of Care Among Patients Referred to Rheumatologists in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: Our aim was to characterize referrals to rheumatologists, the early care management of patients with rheumatic diseases, and timeliness of care and treatment. METHODS: We conducted a retrospective observational study involving patients with first-time rheumatology referrals between 2000 and 2013 in the primary care Electronic Medical Record Administrative data Linked Database (EMRALD) in Ontario, Canada. Referrals were characterized in terms of diagnoses, patient demographics, diagnostic tests, treatment initiated by family physicians and rheumatologists, and other specialists seen prior to rheumatology consultation. Timeliness of referrals, rheumatologist consultations, and treatment were determined overall and for each diagnostic category. RESULTS: Among 2,430 patients referred to a rheumatologist, 69% were female, with an average age of 53 years. The principal diagnosis associated with the referral included osteoarthritis (32%), systemic inflammatory rheumatic diseases (31%), regional musculoskeletal conditions (16%), chronic pain conditions (14%), osteoporosis (2%), and other/miscellaneous (5%). Family physicians most frequently prescribed nonsteroidal antiinflammatory drugs/cyclooxygenase 2 inhibitors (38%), and their pre-referral diagnostic testing practice varied considerably. The duration of time from symptom onset to rheumatology consultation varied by diagnoses, with the shortest being for patients with systemic rheumatic diseases; for rheumatoid arthritis (RA), the median time to consultation was 327 days. Most of the delay occurred prior to referral; 36% of RA patients initiated a disease-modifying antirheumatic drug within 6 months of symptom onset. CONCLUSION: Approximately 1 in 3 referrals to rheumatologists were for a systemic inflammatory rheumatic disease. We observed substantial delays to rheumatology consultations and variations in patterns of care that could be amenable to quality improvement interventions.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".