Predicting Depression in Parkinson’s Disease Using Commonly Available PD Questionnaires
Bibliographic record
Abstract
Background: Depression is common in patients with Parkinson’s disease (PD) and significantly impacts both the patients and their caregivers. The associations between depression and the responses from commonly used questionnaires for PD patients were assessed. New patients presenting to the Movement Disorder Center completed a number of questionnaires, including assessments of the motor and non-motor symptoms of PD, including depression. Methods: The PD patients were grouped according to severity of depression: none, mild, and moderate–severe, based on the Geriatric Depression Scale (GDS) scores. The mean scores of the Unified PD Rating Scale (UPDRS), Montreal Cognitive Assessment (MoCA), Epworth Sleepiness Scale (ESS), Non-motor Symptoms Scale (NMSS), PD Quality of life (PDQ-39), Hoehn and Yahr score (H&Y), levodopa equivalent daily dose (LEDD), and number of antidepressants used were collected. There were 1214 PD patients included. Results: Increasing depression scores were associated with worsening motor symptoms (according to the UPDRS and H&Y), non-motor symptoms (according to the NMSS), cognition (according to the MoCA), sleepiness (according to the ESS), and quality of life (according to the PDQ-39) (all p-values of p < 0.001). Only half of the patients with mild or moderate–severe depression were taking antidepressants, and the LEDD increased with depression severity. The risk of depression increased by 16% and 5% for every 1-point increase in the NMSS and PDQ-39 scores, respectively. Conclusions: Depression is often unrecognized and undertreated and should be assessed regularly in PD patients, especially in those who demonstrate changes in motor or non-motor symptoms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".