The Effect of Triage Assessments on Identifying Inflammatory Arthritis and Reducing Rheumatology Wait Times in Ontario
Bibliographic record
Abstract
OBJECTIVE: We evaluated the influence of triage assessments by extended role practitioners (ERP) on improving timeliness of rheumatology consultations for patients with suspected inflammatory arthritis (IA) or systemic autoimmune rheumatic diseases (SARD). METHODS: Rheumatologists reviewed primary care providers' referrals and identified patients with inadequate referral information, so that a decision about priority could not be made. Patients were assessed by an ERP to identify those with IA/SARD requiring an expedited rheumatologist consult. The time from referral to the first consultation was determined comparing patients who were expedited to those who were not, and to similar patients in a usual care control group identified through retrospective chart review. RESULTS: Seven rheumatologists from 5 communities participated in the study. Among 177 patients who received an ERP triage assessment, 75 patients were expedited and 102 were not. Expedited patients had a significantly shorter median (interquartile range) wait time to rheumatologist consult: 37.0 (24.5-55.5) days compared to non-expedited patients [105 (71.0-135.0) days] and controls [58.0 (24.0-104.0) days]. Accuracy comparing the ERP identification of IA/SARD to that of the rheumatologists was fair (κ 0.39, 95% CI 0.25-0.53). CONCLUSION: Patients triaged and expedited by ERP experienced shorter wait times compared to usual care; however, some patients with IA/SARD were missed and waited longer. Our findings suggest that ERP working in a triage role can improve access to care for those patients correctly identified with IA/SARD. Further research needs to identify an ongoing ERP educational process to ensure the success of the model.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".