A systematic review of the effectiveness of digital cognitive assessments of cognitive impairment in Parkinson’s disease
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it