Psoriatic arthritis screening tools: study design and methodological challenges
Bibliographic record
Abstract
Funding sources: No external funding. Conflicts of interest: A.A.Q. has licensed the PASE questionnaire to Merck and Pfizer, has received a grant from Amgen, and is a consultant for Jansen, Novartis and Abbott. J.F.M. is a consultant for Biogen IDEC, an investigator for Amgen and has served on an advisory board for Amgen. M.E.H. has licensed the PASE questionnaire as above, and has also acted as a consultant for UCB, Amgen, Novartis, Bristol Myers Squibb and Abbott. Dear Editor, In their study ‘Comparison of three screening tools to detect psoriatic arthritis in patients with psoriasis (CONTEST study)’, Coates et al.1 compared three published, validated screening tools for psoriatic arthritis (PsA), the Psoriasis Epidemiology Screening project (PEST), the Toronto Psoriatic Arthritis Screen (ToPAS) and the Psoriatic Arthritis Screening and Evaluation (PASE) tools.1 2 3 4 5 We would like to address how PASE was not adequately or appropriately compared in this setting.
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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 | 0.467 | 0.703 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".