Feasibility of Physiotherapist-Led Rheumatology Triage: A Randomized Study
Bibliographic record
Abstract
Objective Given global shortages in the rheumatology workforce, the demand for rheumatology assessment often exceeds the capacity to provide timely access to care. Accurate triage of patient referrals is important to ensure appropriate utilization of finite resources. We assessed the feasibility of physiotherapist (PT)-led triage using a standardized protocol in identifying cases of inflammatory arthritis (IA), as compared to usual rheumatologist triage of referrals for joint pain, in a tertiary care rheumatology clinic. Methods We performed a single-center, prospective, nonblinded, randomized, parallel-group feasibility study with referrals randomized in a 1:1 ratio to either PT-led vs usual rheumatologist triage. Standardized information was collected at referral receipt, triage, and clinic visit. Rheumatologist diagnosis was considered the gold standard for diagnosis of IA. Results One hundred two referrals were randomized to the PT-led triage arm and 101 to the rheumatologist arm. In the PT-led arm, 65% of referrals triaged as urgent were confirmed to have IA vs 60% in the rheumatologist arm (P= 0.57), suggesting similar accuracy in identifying IA. More referrals were declined in the PT-led triage arm (24 vs 8,P= 0.002), resulting in fewer referrals triaged as semiurgent (6 vs 23,P= 0.003). One case of IA (rheumatologist arm) was incorrectly triaged, resulting in significant delay in time to first assessment. Conclusion PT-led triage was feasible, appeared as reliable as rheumatologist triage of referrals for joint pain, and led to significantly fewer patients requiring in-clinic visits. This has implications for waitlist management and optimal rheumatology resource utilization.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".