Improving Parkinson's Disease Care through Systematic Screening for Depression
Bibliographic record
Abstract
BACKGROUND: Depression is common in Parkinson's disease (PD) but is underrecognized clinically. Although systematic screening is a recommended strategy to improve depression recognition in primary care practice, it has not been widely used in PD care. METHODS: The 15-item Geriatric Depression Scale (GDS-15) was implemented at 5 movement disorders clinics to screen PD patients. Sites developed processes suited to their clinical workflow. Qualitative interviews with clinicians and patients provided information on feasibility, acceptability, and perceived utility. RESULTS: Prior to implementation, depression screening was recorded in 12% using a formal instrument; 64% were screened informally by clinical interview, and no screening was recorded in 24%. Of 1406 patients seen for follow-up care during the implementation period, 88% were screened, 59% using the GDS-15 (self-administered in 51% and interviewer administered in 8%), a nearly 5-fold increase in formal screening. Lack of clinician or staff time and inability to provide the GDS-15 to the patient ahead of the visit were the most commonly cited reasons for lack of screening using the GDS-15; 378 (45%) patients completing the GDS-15 screened positive for depression, and 137 were enrolled for a 12-month prospective follow-up. Mean GDS-15 scores improved from 8.8 to 7.0 (P < 0.0001) and the 39-item Parkinson's Disease Questionnaire emotional subscore from 42.2 to 36.7 (P = 0.0007). CONCLUSIONS: Depression screening in PD using a formal instrument can be achieved at much higher levels than is currently practiced, but there are barriers to implementing this in clinical practice. An individual site-specific process is necessary to optimize screening rates.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".