Validation of the Dutch version of the King’s Parkinson’s disease pain scale
Bibliographic record
Abstract
BACKGROUND: Pain in patients with Parkinson's disease (PD)is often underdiagnosed and, therefore, undertreated. The King's Parkinson's Pain Scale (KPPS) is one of the few validated tools specifically designed to assess pain in patients with Parkinson's disease but lacks a Dutch version. This study aims to validate the KPPS for patients in the Netherlands and to examine which cognitive functions are related to the comprehension of the KPPS. METHODS: The KPPS was translated into Dutch and validated in 70 patients with PD through internal consistency, convergent and discriminant validity testing. Patients had been diagnosed with PD for an average of 5.65 years. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). RESULTS: The Dutch KPPS showed acceptable reliability (Cronbach's alpha = 0.69), though its factor structure differed from the original. Convergent validity was confirmed via significant correlations with the Numerical Rating Scale (NRS), while discriminant validity was supported through correlations with the Non-Motor Symptoms Scale (NMSS) and EQ-5D-3 L. Verbal memory and abstract thinking showed a tendency toward significance in their association with pain scores. CONCLUSION: The Dutch KPPS is a reliable and valid tool for assessing pain in Dutch patients with PD, though its structure differs from the original. These differences may reflect variability in pain perception or classification, highlighting the need for further research integrating the PD-PCS framework to refine pain assessment 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 | 0.007 | 0.019 |
| 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 | 0.001 |
| Open science | 0.001 | 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".