A systematic review of the effectiveness of digital cognitive assessments of cognitive impairment in Parkinson’s disease
Bibliographic record
Abstract
Background: Digitalization in healthcare has been extended to how we examine and manage Parkinson’s Disease Mild Cognitive Impairment (PD-MCI). Methods: Moyer Population (those with PD and in some cases control groups), Intervention (digital cognitive test) and Outcome (validity and reliability) (PIO) and Campbell et al. Synthesis Without Meta-analysis (SWiM) methods were employed. A literature search of MEDLINE, PsycINFO, CINAHL, OpenGrey, and ProQuest Theses and Dissertations Sources screened for articles. Results: The digital trail-making test (dTMT) was the most used measure. There was strong validity between the dTMT and pencil-paper TMT, Mini-Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA) scores (ranging from r = .55 to .90, p < .001). Validity between the TMT pencil-paper and digital versions were adequate (ranging from r = .51 to 90, p < .001). Reliability was demonstrated between PD and control groups’ scores (ranging from r = .71 to .87). One study found excellent inter-rater reliability (ICC = .90 to .95). The dMoCA was the most used screen that assessed more than two cognitive domains. There was a range in the strength of agreement between digital and pencil-paper versions (ICC scores = .37 to .83) and only one study demonstrated adequate validity (r = .59, p < .001). Poor internal consistency (α = .54) and poor test re-test reliability (between PD and control groups’ scores, p > .05) were found. Conclusion: This review found that digitalized cognitive tests are valid and reliable methods to assess PD-MCI. Considerations for future research are discussed.
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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 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".