The Parkinson disease pain classification system: results from an international mechanism-based classification approach
Bibliographic record
Abstract
ABSTRACT: Pain is a common nonmotor symptom in patients with Parkinson disease (PD) but the correct diagnosis of the respective cause remains difficult because suitable tools are lacking, so far. We developed a framework to differentiate PD- from non-PD-related pain and classify PD-related pain into 3 groups based on validated mechanistic pain descriptors (nociceptive, neuropathic, or nociplastic), which encompass all the previously described PD pain types. Severity of PD-related pain syndromes was scored by ratings of intensity, frequency, and interference with daily living activities. The PD-Pain Classification System (PD-PCS) was compared with classic pain measures (ie, brief pain inventory and McGill pain questionnaire [MPQ], PDQ-8 quality of life score, MDS-UPDRS scores, and nonmotor symptoms). 159 nondemented PD patients (disease duration 10.2 ± 7.6 years) and 37 healthy controls were recruited in 4 centers. PD-related pain was present in 122 patients (77%), with 24 (15%) suffering one or more syndromes at the same time. PD-related nociceptive, neuropathic, or nociplastic pain was diagnosed in 87 (55%), 25 (16%), or 35 (22%), respectively. Pain unrelated to PD was present in 35 (22%) patients. Overall, PD-PCS severity score significantly correlated with pain's Brief Pain Inventory and MPQ ratings, presence of dyskinesia and motor fluctuations, PDQ-8 scores, depression, and anxiety measures. Moderate intrarater and interrater reliability was observed. The PD-PCS is a valid and reliable tool for differentiating PD-related pain from PD-unrelated pain. It detects and scores mechanistic pain subtypes in a pragmatic and treatment-oriented approach, unifying previous classifications of PD-pain.
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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 | 0.001 |
| 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".