Rating Scales for Pain in Parkinson's Disease: Critique and Recommendations
Bibliographic record
Abstract
BACKGROUND: We aimed at critically appraising the clinimetric properties of existing pain scales or questionnaires and to give recommendations for their use in Parkinson's disease (PD). METHODS: Clinimetric properties of pain scales used in PD were systematically evaluated. A scale was classified as 'recommended' if was used in PD, showed adequate clinimetric properties, and had been used by investigators other than the original developers; as 'suggested' if it was used in PD and fulfilled only one other criterion; and as 'listed' if it was used in PD but did not meet the other criteria. Only scales rating pain intensity or for syndromic classification were assessed. RESULTS: Eleven of the 34 scales initially considered fulfilled inclusion criteria. Among the scales rating pain intensity, the "Brief Pain Inventory short form," "McGill Pain Questionnaire short and long forms," "Neuropathic Pain Symptoms Inventory," "11-point Numeric Rating Scale," "10-cm Visual Analog Scale," and "Pain-O-Meter" were "recommended with caution" because of lack of clinimetric data in PD, whereas the "King's PD Pain Scale" was "recommended." Among scales for pain syndromic classification, the "DN4" was "recommended with caution" because of lack of clinimetric data in PD; the "Leeds Assessment of Neuropathic Symptoms and Signs," "Pain-DETECT," and the "King's PD Pain Scale" were "suggested." CONCLUSIONS: King's PD pain scale can be recommended for the assessment of pain intensity in PD. Syndromic classification of pain in PD may be achieved by the DN4, but clinimetric data in PD are needed for this scale.
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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 | 0.181 | 0.407 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.011 | 0.003 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".