The sensitivity and specificity of pain diagrams in rheumatic disease referrals
Bibliographic record
Abstract
OBJECTIVES: To determine patterns on pain diagrams and corresponding diagnoses in patients referred to a rheumatology clinic and their sensitivity, specificity, and positive and negative predictive values (PPV and NPV, respectively). METHODS: All new adult patient referrals from two rheumatologists over 6 years were reviewed and eligible if a pain diagram had been completed and they were not previously diagnosed with a rheumatic disease. Pain diagrams completed by the patient were organized into patterns based on the location of joint and/or soft tissue areas by two independent observers. RESULTS: A total of 1101 patients were included. Five major patterns evolved: soft tissue (widespread pain or regional pain such as an entire arm) (n = 236), symmetrical articular (n = 647), asymmetrical articular (n = 136), monoarticular (n = 35) and back (n = 46); 480 had inflammatory arthritis, of whom 121 had RA, 35 PsA, 46 SpAs and 63 crystal arthropathy. FM or chronic pain also occurred in 63 and 25 had PMR. The sensitivity, specificity, PPV and NPV for polyarticular symmetrical pattern in RA was 87.6, 44.7, 16.4 and 96.7% and for inflammatory arthritis with symmetrical or asymmetrical pattern was 82.3, 37.4, 50.4 and 73.2%; and a back pattern in AS was 10.9, 98.9, 41.7 and 96.2%. Inter-rater reliability was high for monoarticular, symmetrical and asymmetrical patterns (intra-class correlation coefficient ≥ 0.777). CONCLUSION: Pain diagram patterns may help to increase the likelihood of various rheumatic diagnoses including polyarticular pattern and inflammatory arthritis, and there was high inter-rater reliability. However, testing the value of pain diagrams in addition to a referral note is necessary to determine if they have added value.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".