[Burden and related factors among care-takers of patients with Parkinson's disease].
Bibliographic record
Abstract
OBJECTIVE: To assess the impact on caretaker who looked after patients with Parkinson's disease (PD) and to identify the main factors related to their burden. METHODS: 115 consecutive pairs of PD patients and their caretakers were included. Caregiver Burden Inventory (CBI) was used to assess the burden of PD on the caretakers. Patients were evaluated by neurologists using the United Parkinson's Disease Rating Scale (UPDRS), the Hoehn and Yahr Scale (H-Y Scale), the Activity of Daily Living Scale (ADL), the Parkinson's Disease Questionnaire (PDQ-39), the Hamilton Depression Rating Scale (HAMD), the Hamilton Anxiety Rating Scale (HAMA), the Montreal Cognitive Assessment (MoCA) and the Mini-mental State Examination (MMSE). Multiple linear stepwise regression models were fitted to ascertain the factors linked to the CBI. RESULTS: Based on multiple linear stepwise regression analysis, ADL (β = -0.813, t = -6.265, P = 0.000) and PDQ-39 (β = 0.285, t = 4.256, P = 0.000) of patients and the age of caretakers (β = 0.327, t = 3.107, P = 0.002) proved to be the main predictors of CBI. CONCLUSION: Many factors might comprehensively affect the burden of PD on caretakers of the patients. Attention needs to be given to the early identification of factors that generating stress on caretakers in order to improve their quality of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".