Pain characterization in patients with Parkinson's disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES:Pain in Parkinson's disease is a very frequent complaint and may precede the diagnoses of the disease. This study aimed at evaluating pain in a group of Parkinson's disease patients from a specialized treatment center.METHODS:This is a observational study of pain in Parkinson's disease patients from the Clinicas Hospital, Federal University of Pernambuco. The convenience sample, obtained between July and August 2011, was made up of 24 individuals, being 17 males and 7 females, aged between 42 and 50 (mean=64.3) years, and 48 and 66 (mean=58.7) years, respectively. Section III of the Unified Parkinson's Disease Rating Scale, Hoehn and Yahr (HY) scale according to the stage of the disease, McGill pain questionnaire and Mini Mental State Examination were used.RESULTS:Specific body region with most frequent pain was lumbar spine (50%). Categorized regions with highest complaint percentages were: trunk (66.7%) and limbs (37.5% upper; 37.5% lower). Most patients have referred pain in a single body region, regardless of analyzing specific or categorized regions (37.5%). There has been no significant difference in proportional scores obtained by each McGill questionnaire score component. Patients with rigid-akinetic Parkinson's disease had higher number of painful body regions. The comparison among McGill indices, according to predominant symptom and according to Parkinson's disease stage (HY) scores has not shown significant differences.CONCLUSION:In our study, all Parkinson's disease patients have referred pain. Although pain is one of the most frequent non-motor symptoms, many aspects regarding Parkinson's disease-related pain need further investigation, such as which would be the best pain categorization and which methodology could better distinguish different mechanisms of different types of pain.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".