Investigation of symptoms and coping styles of Parkinson's disease patients with different clinical stages
Bibliographic record
Abstract
ObjectiveTo investigate the occurrence of motor symptoms and non⁃motor symptoms in patients with different clinical stages of Parkinson's disease，to understand the characteristics of coping styles in patients with different clinical stages of Parkinson's disease，and to analyze the correlation between coping styles and motor symptoms and non⁃motor symptoms.MethodsFrom March to October 2023，a total of 190 patients with Parkinson's disease were selected from the neurology department of a tertiary grade A hospital in Shanxi province.The basic data of the patients were collected，the clinical stages of the patients were divided by the modified Hoehn⁃Yahr（H⁃Y） staging scale，and the motor symptoms of the patients were evaluated by the Unified Parkinson Disease Rating Scale（UPDRS）.The Pittsburgh Sleep Quality Index（PSQI），Montreal Cognitive Assessment（MoCA），Hamilton Anxiety Scale（HAMA），Hamilton Depression Scale（HAMD），Modified Apathy Evaluation Scale（MAES），King's Parkinson's Disease Pain Scale（KPPS），and the Argentina Hyposmia Rating Scale（AHRS） were used to evaluate the patients' non⁃motor symptoms from four aspects：sleep，cognition，mental function，and sensation.The Medical Coping Modes Questionnaire（MCMQ） was used to evaluate the coping style of the patients，and the correlation between the coping style and motor and non⁃motor symptoms was analyzed.ResultsAmong 190 patients with Parkinson's disease，138 patients had an early stage，35 patients had a middle stage，and 17 patients had an advanced stage.The total score of avoidance dimension in early Parkinson's disease patients was positively correlated with pain（P<0.05） and negatively correlated with smell（P<0.05），and the total score of yield dimension was positively correlated with sleep（P<0.05）.The total score of face dimension was positively correlated with cognition in patients with middle and advanced Parkinson's disease（P<0.05）.ConclusionsThe more negative coping styles in early Parkinson's disease patients choose，the higher the degree of pain and the worse the quality of smell and sleep.The more active coping styles in patients with middle⁃ and advanced⁃stage Parkinson's disease chose，the higher their cognitive level.Caregivers should develop personalized sleep plans and medication regimens to enhance patients' rehabilitation and cognitive training and improve non⁃motor symptoms in patients with Parkinson's disease.
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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 | 0.000 |
| 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) | 0.001 | 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".