Psoriasis Epidemiology Screening Tool (PEST): A Report from the GRAPPA 2009 Annual Meeting
Bibliographic record
Abstract
Patients with psoriasis attending general practitioner and dermatology clinics may complain about their joints, but it may be difficult for the nonrheumatologist to distinguish psoriatic arthritis (PsA) from other forms of arthritis. A screening tool for PsA would therefore be useful to both general practitioners and dermatologists and help identify patients for further evaluation by a rheumatologist. Although several screening tools have been developed, the Psoriasis Epidemiology Screening Tool (PEST) has the advantage of simplicity and ease of use. This new instrument consists of 5 simple questions supported by the addition of a manikin for patient markup. During development, the questionnaire has shown a sensitivity of 0.94 and a specificity of 0.78. Further validation of this and the other questionnaires is now required. A "head to head" study of the PEST, ToPAS (Toronto Psoriatic Arthritis Screening questionnaire), and PASE (Psoriatic Arthritis Screening and Evaluation) tools is planned in a secondary-care population with psoriasis. This study is important not only to confirm the comparative performance of the instruments, but also to confirm the high figures for sensitivity in a secondary-care population.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".