The Development and Validation of a Novel Training Infographic for the Physician Global Visual Analog Scale in Psoriatic Arthritis
Bibliographic record
Abstract
Objective Psoriatic arthritis (PsA) is a heterogenous condition with musculoskeletal and skin manifestations. The physician global visual analog scale (VAS) is an important component of many composite scores used in clinical trials and observational studies. Currently, no training material exists to standardize this assessment. Methods The Psoriatic Arthritis Validation of Physician Global VAS (PAVLOVAS) project describes the development of a novel training infographic with stakeholder involvement, which was then evaluated in a Latin square design in which 20 patients with PsA were assessed by 10 clinicians. For each group of 10 patients, 5 assessors conducted traditional assessment (consisting of 66/68-joint count, body surface area, Leeds Enthesitis Index, and dactylitis and nail counts) and 5 assessors conducted a standardized, thorough general examination informed by the infographic. Assessors switched assessment type between groups. The 3-item (3VAS) and 4VAS informed by traditional and infographic methods were compared, alongside other composite scores. Results There was strong agreement between traditional and infographic physician global VAS (intraclass correlation coefficient [ICC] 0.69, P = 0.01). This improved to very strong agreement when incorporated into the 3VAS (ICC 0.99, P < 0.001) and 4VAS (ICC 0.99, P < 0.001). The duration of assessment was significantly less for the infographic vs traditional groups (6.5 vs 7.8 mins, P < 0.001). There was moderately high agreement between the 3VAS and 4VAS categories of disease activity, with the same categories defined by Psoriatic Arthritis Disease Activity Score (PASDAS) and Disease Activity Index for Psoriatic Arthritis (DAPSA; χ2 17.0, P = 0.049). Conclusion Our group developed and validated a novel training infographic that informs a briefer assessment of the physician global VAS than traditional assessments. This tool has potential applications in training and routine clinical practice.
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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 | 0.035 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".