Bibliographic record
Abstract
Objective To investigate factors associated with the quality of life(QOL) in patients with PD,especially the relationship between QOL and non-motor symptoms(NMS).Methods 185 PD patients were analyzed with UPDRS-Ⅲ and H-Y for assessing their motor symptoms,meanwhile,the PDNMS Questionnaire(NMSQ),Hamilton Depression Scale(HAMD),Hamilton Anxiety Scale(HAMA),Parkinson Disease Sleep Scale(PDSS) and Montreal cognitive assessment(MOCA) were used for their NMS.The QOL was assessed with Medical Outcomes Study 36-Item Short-Form Health Survey(SF-36).Multiple linear regression analyses were used to determine which variables were strongly associated with QOL.Results Patients with PD had lower scores on all dimensions of SF-36,comparing with those of the healthy old ones.The total score of SF-36 had negative associations with scores of H-Y stages,UPDRSⅢ,HAMA,HAMD and NMSQ,and a positive one with PDSS.Multiple stepwise regression analysis showed that scores of UPDRSⅢand rigidity could explain 21.6%of the variance of SF-36 total score.When non-motor scales were included in the model,NMSQ alone could explain 21.5%.After we instead the HAMD and NMSQ with their subdomains,feelings of despair,urinary symptoms,cognitive impairment,weight loss and blocking accounted for 35.8%of the variance.For patients below 65 years old,66～75 years old and older than 75years old,predictors were different.Conclusion The NMS such as feelings of despair,urinary symptoms,cognitive impairment,weight loss and blocking are the more important predictors of deterioration in QOL in PD patients.Given the patient's traits,treatments aiming to these aspects should be individualized.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".