Physical Therapists’ Ability to Distinguish Between Inflammatory and Noninflammatory Arthritis and to Appropriately Refer Patients to a Rheumatologist
Bibliographic record
Abstract
OBJECTIVE: To investigate whether physical therapists (PTs) can correctly identify new-onset inflammatory arthritis; to assess whether PTs are aware that cases of new-onset inflammatory arthritis should be referred to a rheumatologist; to explore the comfort level of PTs to refer to medical specialists; and to determine factors associated with correctly identifying inflammatory arthritis and referring to a rheumatologist. METHODS: We sent a questionnaire to PTs in 2 Canadian provinces describing 4 case scenarios (new-onset rheumatoid arthritis [RA], knee osteoarthritis [OA], new-onset ankylosing spondylitis [AS], and low back pain [LBP]). Participants were asked to identify probable medical diagnoses and indicate their plan of action. We described the frequencies of our outcomes and used logistic regression to explore associated factors. RESULTS: A total of 352 PTs responded. The proportions who correctly identified each of the 4 cases were 90%, 83%, 77%, and 100%, respectively, for RA, OA, AS, and LBP. Among those, 77%, 30%, 73%, and 3%, respectively, indicated that it was "very important" or "extremely important" to refer to a rheumatologist. Approximately two-thirds felt "extremely comfortable" or "quite comfortable" to refer to a specialist. PTs working in rural areas were less likely to refer. CONCLUSION: Most PTs correctly identified the clinical cases and were aware of the importance of prompt referral to a rheumatologist for inflammatory disease. Most indicated that it was not very important to refer those with OA and LBP. This implies that many PTs can distinguish between inflammatory and noninflammatory conditions and appropriately refer patients with suspected inflammatory arthritis to a rheumatologist.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".