Psychometric features of brief pain inventory for Parkinson’s disease during medication states
Bibliographic record
Abstract
PURPOSE: Patients with idiopathic Parkinson's disease (PD) suffer from different non-motor symptoms, including pain. The present study aimed to measure the psychometric properties of the Brief Pain Inventory (BPI) in patients with PD during ON- and OFF-states. METHODS: We recruited 460 patients with PD and 100 non-PD controls. The pain was assessed by the BPI, King's Parkinson's disease Pain Scale (KPPS), Neuropathic Pain Symptom Inventory (NPSI), Visual Analogue Scale-Pain (VAS-pain), and short-form McGill Pain Questionnaire-2 (SF-MPQ-2) in both medication states. Internal consistency and test-retest reliability was examined using Cronbach's alpha coefficient and intra-class correlation coefficient (ICC). Dimensionality and convergent validity of BPI were also investigated. Diagnostic accuracy and discriminative validity were determined by Receiver Operating Characteristics (ROC) curve analysis and Area Under the Curve (AUC). RESULTS: = 0.91-0.97) in both states. The ICC values were 0.85-0.96 in ON- and OFF-state. Factor analysis revealed two factors. A high correlation was obtained between BPI subscales and other scales. AUC >0.91, sensitivity, and specificity> 0.77 were observed for discriminating different pain levels. Furthermore, appropriate diagnostic accuracy was found (AUC, sensitivity, and specificity >0.67) between non-PD control and PD patients. CONCLUSION: The BPI has acceptable psychometric features as well as diagnostic accuracy for patients with PD.Implications for rehabilitationPain as a non-motor symptom in PD can affect daily and social activities.The BPI is used to assess pain severity and interference in activities.For better treatment, pain should be assessed in off-state like to on-state.BPI has satisfactory reliability and validity in different medication states in PD.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".