P.023 The Pain in Dystonia Scale (PIDS): development and validation
Bibliographic record
Abstract
Background: Pain in a common symptom in adult-onset idiopathic dystonia (AOID). An appropriate tool to understand this symptom is needed to improve AOID patients’ care. We developed a rating instrument for pain in AOID and validated it in cervical dystonia (CD). Methods: Development and validation of the Pain in Dystonia Scale (PIDS) in three phases: 1. International experts and participants generated and evaluated the preliminary items for content validity; 2. The PIDS was drafted and revised, followed by cognitive interviews to ensure suitability for self-administration; and 3. the clinimetric properties of the final PIDS were assessed in 85 participants. Results: PIDS evaluates pain severity (by body part), functional impact and external modulating factors. It showed high test-retest reliability the total score (0.9, p<0.001), intraclass correlation coefficients higher than 0.7 for all items and high internal consistency (Cronbach’s alpha 0.9). Convergent validity analysis revealed a strong correlation between the PIDS severity score and the TWSTRS pain subscale (0.8, p<0.001), the brief pain inventory short form (0.7, p<0.001) and impact of pain on daily functioning (0.7, p<0.001). Conclusions: The PIDS is the first specific questionnaire developed to evaluate pain in patients with AOID with high-level clinimetric properties in people with CD.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".