The Romanian Translation and Cultural Adaptation of the Early Arthritis for Psoriatic Patients (EARP) Questionnaire, Psoriasis Epidemiology Screening Tool (PEST), and Toronto Psoriatic Arthritis Screen 2 (ToPAS 2)
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Psoriasis is a chronic inflammatory condition mediated by the immune system with various manifestations. The increased prevalence of subclinical joint involvement has led to the development of early diagnostic methods for psoriatic arthritis, including several instruments that have been validated and used in clinical practice. The aim of this study was to perform the Romanian translation, cultural adaptation, and validation of three assessment tools: the Early Arthritis for Psoriatic Patients (EARP) Questionnaire, Psoriasis Epidemiology Screening Tool (PEST), and Toronto Psoriatic Arthritis Screen 2 (TOPAS 2), which are designed to evaluate early-stage arthritis in patients with psoriasis. METHODS: All the activities were carried out in accordance with the internationally recognized methodology recommended by the International Society for Pharmacoeconomics and Outcome Research (ISPOR), the recommendations of the World Health Organization (WHO) regarding the translation process and the validation of instruments, and data from the international literature. These three questionnaires were administered to 29 patients with psoriasis diagnosed by biopsy. A descriptive study was conducted and the data were analyzed with appropriate statistical tests using the PSPP program. A reliability test was assessed using Cronbach's alpha coefficient. RESULTS: The obtained values were significant for the first two questionnaires, with a value of 0.89 for the EARP and 0.63 for the PEST, but the value was not as significant for ToPAS2, at 0.40. CONCLUSIONS: This pilot study revealed that the Romanian and original versions of the three questionnaires are similar.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".