Evolution of Apathy in Early Parkinson's Disease: A 4-Years Prospective Cohort Study
Bibliographic record
Abstract
Objective: We investigated the prevalence, evolution, associated factors, and risk factors of apathy in a cohort of patients with early-stage Parkinson's disease (PD), who underwent a 4-years prospective follow-up. Methods: This study included 188 patients with PD (baseline disease duration <3 years) who underwent an annual evaluation using the Lille Apathy Rating Scale (LARS). Based on the cut-off value of −21 observed on the LARS, patients were categorized as PD with and without apathy. The generalized estimating equations (GEE) model was utilized to determine the factors associated with apathy, and the Cox proportional-hazards regression model was used to determine the predictors of apathy. Results: Apathy increased from a baseline rate of 18.6–28.8% after 4 years; notably, this rate was not persistent across patients' visits. The LARS score was independently associated with the male sex (B 8.131, p = 0.009), low Frontal Assessment Battery (FAB) scores (B 0.567, p = 0.011), low attention scores on the Montreal Cognitive Assessment (MOCA) test (B 0.217, p = 0.026), high Hamilton Depression Rating Scale (HDRS) scores (B 1.362, p < 0.001), high Unified Parkinson's Disease Rating Scale (UPDRS) part III scores (B 1.147, p < 0.001), and prolonged follow-up time (B 1.785, p = 0.048). A high HDRS score was the only predictor of apathy in PD [hazard ratio (HR) 1.043, p = 0.026]. Conclusions: The risk of apathy is higher in men with progressive PD accompanied by disease-specific motor and non-motor symptoms. Depression during early-stage PD is a primary risk factor for apathy in PD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".