Diagnosis, treatment and management of apathy in Parkinson’s disease: a scoping review
Bibliographic record
Abstract
OBJECTIVE: To conduct a scoping review of the literature on apathy in Parkinson's disease (PD), to better understand how apathy in Parkinson's disease is diagnosed, treated and managed. METHODS: MEDLINE, Embase, PsycINFO, CINAHL, Cochrane Central Register of Control Trials and Cochrane Database of Systematic Reviews were searched to 17 May 2017. An updated review was run from 17 May 2017 to 28 January 2019. The grey literature was searched using the CADTH Grey Matters tool. Original peer-reviewed research was included if it included individuals with PD and apathy. Non-original data was only included if it was in the form of meta-analysis. All information regarding diagnosis, treatment and management of PD was extracted. Citation screening and extraction were performed in duplicate. RESULTS: From 11 375 citations, 362 articles were included in the final review. The majority of included studies focussed on prevalence, with few studies examining treatment. Twenty screening tools for apathy were identified. Fifty per cent of treatment studies were randomised control trials (RCTs). RCTs applied treatment methods including: exercise, mindfulness, rotigotine (Neupro) transdermal patch and rivastigmine (Exelon). CONCLUSIONS: This review identified a large body of literature describing current knowledge on diagnosing, treating and managing apathy in PD. Future research should aim to detect an ideal screening tool for apathy in PD, to identify the best treatment options for apathy and the variety of comorbidities it may present with and finally aim to better understand postoperative apathy in those with deep brain stimulation.
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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 | 0.017 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".