What contributes to quality of life in patients with Parkinson's disease?
Bibliographic record
Abstract
OBJECTIVE: To identify the factors that determine quality of life (QoL) in patients with idiopathic Parkinson's disease in a population based sample. Quality of life (QoL) is increasingly recognised as a critical measure in health care as it incorporates the patients' own perspective of their health. METHODS: All patients with Parkinson's disease seen in a population based study on the prevalence of parkinsonism were asked to complete a disease-specific QoL questionnaire (PDQ-39) and the Beck depression inventory. A structured questionnaire interview and a complete neurological examination, including the Hoehn and Yahr scale, the Schwab and England disability scale, the motor part of the unified Parkinson's disease rating scale (UPDRS part III), and the mini mental state examination were performed by a neurologist on the same day. RESULTS: The response rate was 78%. The factor most closely associated with QoL was the presence of depression, but disability, as measured by the Schwab and England scale, postural instability, and cognitive impairment additionally contributed to poor QoL. Although the UPDRS part III correlated significantly with QoL scores, it did not contribute substantially to predicting their variance once depression, disability, and postural instability had been taken into account. In addition, patients with akinetic rigid Parkinson's disease had worse QoL scores than those with tremor dominant disease, mainly due to impairment of axial features. CONCLUSION: Depression, disability, postural instability, and cognitive impairment have the greatest influence on QoL in Parkinson's disease. The improvement of these features should therefore become an important target in the treatment of the 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".