P.024 Pain in monogenic Parkinson’s Disease
Bibliographic record
Abstract
Background: Pain is one of the most bothersome symptoms reported in Parkinson’s disease (PD), yet its underlying pathophysiological mechanisms are not well understood. Its prevalence and effects on quality of life in patients with monogenic forms of PD have not been systematically explored. Methods: Comprehensive literature review exploring the association between monogenic forms of PD (SNCA, PRKN, PINK1, DJ1, and LRRK2) and pain. We included pain in ATP13A2, VPS35, and GBA1 mutation carriers. After initial screening, sixty-five relevant articles were identified. Studies’ design, sample sizes, and pain outcome measures were highly heterogeneous. Results: Our review suggests that patients with some PD monogenic causes show a higher prevalence of specific pain subtypes. While painful foot dystonia is more frequently reported in SNCA and PRKN carriers, the last ones also describe frequent lower back pain mostly. Pain in general is most commonly reported in PINK1 mutation carriers followed by patients with LRRK2 mutations. Pain as an initial symptom and severe symptom is well described in GBA1-PD patients. There is limited and insufficient evidence to report on pain and ATP13A2, DJ1, and VPS35 mutations. Conclusions: Linking genetic profiles to pain outcomes may have a meaningful clinical impact, facilitating individualized treatment for 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".