Comprehensive Arthritis Referral Study — Phase 2: Analysis of the Comprehensive Arthritis Referral Tool
Bibliographic record
Abstract
OBJECTIVE: Rheumatologists triage referrals to assess those patients who may benefit from early intervention. We describe a referral tool and formally evaluate its sensitivity for urgent and early inflammatory arthritis (EIA) referrals. METHODS: All referrals received on a standardized referral tool were reviewed by a rheumatologist and, based on the information conferred, assigned a triage grade using a previously described triage system. Each referral was also dichotomized as suspected EIA or not. After the initial rheumatologic assessment, the diagnosis was recorded and a consultation grade, blinded to referral grade, was assigned to each case. Agreement between referral and consultation grades was assessed. A regression analysis was performed to determine factors that predicted truly urgent referrals including EIA. RESULTS: We evaluated 696 referrals. A total of 210 (30.2%) were categorized as urgent at the time of consultation. The referral tool was able to successfully detect 169 of these referrals (sensitivity 80.5%, specificity 79.4%). EIA occurred in 95 (13.6%); of those referrals, 86 were correctly classified as urgent at the time of triage (sensitivity 90.5%, specificity 69.6%). Items that helped correctly discriminate urgent or EIA referrals included patient age < 60, duration of disease, morning stiffness, patient-reported joint swelling, a personal or family history of psoriasis, urgency as rated by referring physician, prior assessment by a rheumatologist, elevated C-reactive protein, and a positive rheumatoid factor. CONCLUSION: A 1-page referral tool that includes parts completed by the referring physician and patient has good sensitivity to detect urgent referrals including EIA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".