King's Parkinson's disease pain scale, the first scale for pain in PD: An international validation
Bibliographic record
Abstract
Pain is a key unmet need and a major aspect of non-motor symptoms of Parkinson's disease (PD). No specific validated scales exist to identify and grade the various types of pain in PD. We report an international, cross-sectional, open, multicenter, one-point-in-time evaluation with retest study of the first PD-specific pain scale, the King's PD Pain Scale. Its seven domains include 14 items, each item scored by severity (0-3) multiplied by frequency (0-4), resulting in a subscore of 0 to 12, with a total possible score range from 0 to 168. One hundred seventy-eight PD patients with otherwise unexplained pain (age [mean ± SD], 64.38 ± 11.38 y [range, 29-85]; 62.92% male; duration of disease, 5.40 ± 4.93 y) and 83 nonspousal non-PD controls, matched by age (64.25 ± 11.10 y) and sex (61.45% males) were studied. No missing data were noted, and floor effect was observed in all domains. The difference between mean and median King's PD Pain Scale total score was less than 10% of the maximum observed value. Skewness was marginally high (1.48 for patients). Factor analysis showed four factors in the King's PD Pain Scale, explaining 57% of the variance (Kaiser-Mayer-Olkin, 0.73; sphericity test). Cronbach's alpha was 0.78, item-total correlation mean value 0.40, and item homogeneity 0.22. Correlation coefficients of the King's PD Pain Scale domains and total score with other pain measures were high. Correlation with the Scale for Outcomes in PD-Motor, Non-Motor Symptoms Scale total score, and quality of life measures was high. The King's PD Pain Scale seems to be a reliable and valid scale for grade rating of various types of pain 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".