Comparative Efficacy of Different Triage Methods for Psoriatic Arthritis: Results From a Prospective Study in a Rapid Access Clinic
Bibliographic record
Abstract
OBJECTIVE: We undertook this study to identify the optimal combination of triage methods to identify psoriatic arthritis (PsA) among psoriasis patients with musculoskeletal symptoms in a rapid access clinic and to describe their outcome after 1 year. METHODS: Patients with psoriasis and no prior diagnosis of PsA were referred for assessment of their musculoskeletal symptoms. Each patient was assessed by the following 3 triage modalities: 1) assessment by an advanced practice physical therapist; 2) targeted musculoskeletal ultrasound (MSK-US); and 3) PsA screening questionnaires. The patients were then evaluated by a rheumatologist who determined the patient's disease status and classified them into the following groups: not PsA, possibly PsA, or PsA. Patients returned for a 1-year follow-up visit and were reassessed for change in their disease status. Sensitivity and specificity were calculated for each individual modality, as well as for combinations of modalities. RESULTS: A total of 203 patients with psoriasis and musculoskeletal symptoms were enrolled. The percentage of patients classified as having PsA was 8.8%, and 23.6% were converted into the possibly PsA group. There was no significant difference in the individual performance of the modalities. The highest sensitivity was seen with MSK-US (89%), and the highest specificity was found with the Psoriatic Arthritis Screening and Evaluation questionnaire (79%). The addition of MSK-US data improved the performance of the modalities. A total of 9 patients were classified into the PsA group after 1 year. All patient-reported outcome measures had significantly improved at 1 year (P < 0.001). CONCLUSION: Combining MSK-US with a screening questionnaire for PsA improved the triage of patients with suspected PsA.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".