Detection of dermatological abnormalities in the rheumatology clinic using a standardized screening exam
Bibliographic record
Abstract
AIM: To develop a standardized practical screening tool for rheumatologists to assess for underlying dermatological manifestations of rheumatic conditions. METHODS: A relevant screening tool was developed by consensus between dermatology and rheumatology authors. Patients visiting the general rheumatology clinic for routine care were systematically assessed based on the standardized screening tool. RESULTS: One hundred patients were recruited with 76 being female. The most prevalent rheumatic conditions seen in the clinic were rheumatoid arthritis, psoriatic arthritis and systemic lupus erythematosus. The standardized integumentary assessment took a mean of 2.75 (SD 1.61) min. Most patients, 74%, reported no concerns with their hair or nails, while 60% reported no concerns with their skin. The majority of patients had one abnormality identified, 65%, and of those diagnoses, most affected the skin with 71% of patients having an identified skin abnormality, compared with the hair (10%) or nails (13%). CONCLUSION: The standardized integumentary assessment tool can be successfully incorporated into routine clinical practice for rheumatologists without significant extension of consultation time and may detect relevant abnormalities important for diagnosis which may have been unnoticed by patients. It may encourage collaborative care and enhance clinical outcomes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".