First comprehensive tool for screening pain in Parkinson's disease: the King's Parkinson's Disease Pain Questionnaire
Bibliographic record
Abstract
Background and purpose Pain is highly prevalent in Parkinson's disease ( PD ), impacting patients’ ability, mood and quality of life. Detecting the presence of pain in its multiple modalities is necessary for adequate personalized management of PD . A 14‐item, PD ‐specific, patient‐based questionnaire (the King's Parkinson's Disease Pain Questionnaire, KPPQ ) was designed corresponding to the rater‐based KPP Scale ( KPPS ). The present multicentre study was aimed at testing the validity of this screening tool. Methods First, a comparison between the KPPQ scores of patients and matched controls was performed. Next, convergent validity, reproducibility (test–retest) and diagnostic performance of the questionnaire were analysed. Results Data from 300 patients and 150 controls are reported. PD patients declared significantly more pain symptoms than controls (3.96 ± 2.56 vs. 2.17 ± 1.39; P < 0.0001). The KPPQ convergent validity was high with KPPS total score ( r S = 0.80) but weak or moderate with other pain assessments. Test–retest reliability was satisfactory with kappa values ≥0.65 except for item 5, Dyskinetic pains ( κ = 0.44), and the intraclass correlation coefficient ( ICC ) for the KPPQ total score was 0.98. After the scores of the KPPS were adapted for screening (0, no symptom; ≥1, symptom present), a good agreement was found between the KPPQ and the KPPS ( ICC = 0.88). A strong correlation ( r S = 0.80) between the two instruments was found. The diagnostic parameters of the KPPQ were very satisfactory as a whole, with a global accuracy of 78.3%–98.3%. Conclusions These results suggest that the KPPQ is a useful, reliable and valid screening instrument for pain in PD to advance patient‐related outcomes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".