Towards a better Comprehension and Management of Pain and Psychological Distress in Parkinson’s: The Role of Catastrophizing
Bibliographic record
Abstract
ObjectivesPain is very prevalent in Parkinson’s and challenging to manage. As many people with Parkinson’s (PwP) with pain suffer from anxious and depressive symptoms, we examined the role of catastrophizing in mediating the relationship between pain and psychological distress for this population.Methods169 international PwP completed an online survey with socio-demographic and medical data. Participants completed psychometric tests to assess their pain (King’s Parkinson’s Disease Pain Questionnaire, McGill Pain Questionnaire and Brief Pain Inventory), psychological distress (Beck Depression Inventory and Parkinson Anxiety Scale), pain coping strategies (Coping Strategies Questionnaire) and pain catastrophizing (Pain Catastrophizing Scale).ResultsDepending on the tool used, 82.8% to 95.2% of participants reported pain. 23.5 % and 67.5% of participants showed respectively significant levels of depressive and anxiety symptoms. Psychological distress was significantly correlated with the quality of pain (both sensory and affective dimensions). Statistical models highlighted the mediating role of catastrophizing in the relationship between psychological distress and pain in Parkinson’s.ConclusionThese findings offer new perspectives toward understanding the underlying mechanisms of pain in Parkinson’s and for effective therapeutic intervention goals to facilitate adaptation to pain symptoms in Parkinson’s.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".