Validation of an original questionnaire for patients with psoriatic arthritis: the Psoriatic Arthritis Impact Profile (PAIP).
Bibliographic record
Abstract
BACKGROUND AND AIMS: There is a wide evidence that Psoriatic Arthritis (PsA) as well as Psoriasis (Ps) lead to significant health problems and interfere with the patient quality of life (QoL). Even though a validated questionnaire for Ps is available, no questionnaire for PsA is currently present in literature. The aim of our work has been to confirm the efficacy of our original questionnaire as well as to validate it, through the comparison with other existing recognised and accepted questionnaires, such as MOF-SF36, HAQ, McGill Pain Questionnaire, and Zeung Self-Rating Depression and Anxiety Scales. MATERIALS AND METHODS: We have realized a questionnaire for PsA (Psoriatic Arthritis Impact Questionnaire, PAIP), in terms of psychological and rheumatological evaluation, QoL, social and economic assets. RESULTS: The statistical comparisons between PAIP and the accepted questionnaires (see above) confirm that PAIP is widely validated and represents a useful tool suitable for clinical evaluation and management of patients with PsA. CONCLUSIONS: The indexes of the correlation among the different parts of PAIP and the other questionnaires have shown positive correlations. Moreover, PAIP presents a dedicated unit for the economical and therapeutic parameters, The short time for compilation (15 minutes), the easy of comprehension of the questions, and - above all - the validation of PAIP, make our questionnaire a useful tool, suitable for the clinical management of the patients with PsA.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".