Intercountry comparisons of advanced Parkinson's disease symptoms and management: Analysis from the <scp>OBSERVE‐PD</scp> observational study
Bibliographic record
Abstract
OBJECTIVES: In the absence of widely accepted criteria, determining when a patient with Parkinson's disease (PD) may benefit from more advanced treatments such as device-aided therapy (DAT) so far remains a matter of physician judgment. This analysis investigates how classification of PD varies across countries relative to measures of disease severity. MATERIALS AND METHODS: The OBSERVational, cross-sEctional PD (OBSERVE-PD) study included consecutive patients with PD at centers that offer DATs in 18 countries. In this subgroup analysis, we explore intercountry differences in identification of advanced versus non-advanced PD based on physician's clinical judgment, symptoms assessed using Delphi consensus criteria, use of DAT, motor and non-motor symptoms, and caregiver support. Demographic and clinical characteristics were obtained through review of medical records. RESULTS: Overall, 1342 of 2615 patients (51.3%) were assessed by physicians as having advanced PD. The proportion of patients in different countries identified as having advanced PD (24.4-82.2%) varied. In 15 of 18 countries, a greater proportion of patients with advanced PD, according to select Delphi criteria, were identified by physicians as having advanced PD than with non-advanced PD. There was a wide variability across countries in the proportion of patients with no dyskinesia, disabling dyskinesia, dyskinesia pain, and non-motor symptoms who were identified by physicians as having advanced versus non-advanced PD. CONCLUSIONS: The proportion of patients identified with advanced PD symptoms varies widely across countries, despite differences on the patients' profiles, indicating a need for objective diagnostic criteria to help identify patients who may benefit from DAT.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".