Pain and Parkinson's disease: Current mechanism and management updates
Bibliographic record
Abstract
The aim of this comprehensive review was to provide an overview of pain in Parkinson's disease (PD) by identifying different clinical features and potential mechanisms, and presenting some data on the evaluation and management of pain in PD. PD is a multifocal degenerative and progressive disease, which could affect the pain process at multiple levels. Pain in PD has a multifactorial aetiology, with a dynamic process based on pain intensity, complexity of symptoms, pain pathophysiology and presence of comorbidities. In fact, pain in PD responds to the concept of multimorphic pain, which can evolve, in relation to the different factors, whether they are linked to disease and its management. Understanding the underlying mechanisms will help in guiding of treatment choices. Providing scientific support useful for clinicians and health professionals involved in management of PD, the aim of this review was to bringing practical suggestions and clinical perspectives on the development of a multimodal approach guided by a multidisciplinary clinical intervention through a combination of pharmacological and rehabilitative approaches, to manage pain to improve the quality of life on individuals with PD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".