Digitial memory test for detecting and assessing cognitive impairment in parkinson's disease
Bibliographic record
Abstract
Abstract Background The objectives of the study is to assess the clinical utility of the MemTrax Memory Test for detection of cognitive impairment in patients with PD. Method The MemTrax and Montreal Cognitive Assessment (MoCA) were administered to 61 healthy controls (HC), 102 PD patients with normal cognition (PD‐N), 74 PD patients with mild cognitive impairment (PD‐MCI) and 52 PD patients with dementia (PD‐D). The MemTrax performance, MTx‐%C, MTx‐RT and MTx‐Cp, and the MoCA scores were comparatively analyzed. The MemTrax performances were assessed according to the areas under the receiver operating characteristic curve. Result The MoCA scores were similar between HC and PD‐N, however, MTx‐%C and MTx‐Cp were lower in PD‐N than HC(p<0.05). MTx‐%C, MTx‐Cp and the MoCA scores were significantly lower in PD‐MCI versus PD‐N and in PD‐D versus PD‐MCI (p ≤ 0.001), while MTx‐RT was statistically longer in PD‐D versus PD‐MCI (p ≤ 0.001). For the PD groups, the MemTrax performance strongly correlated with the MoCA scores. To detect PD‐MCI, the optimal MTx‐%C and MTx‐Cp cutoff were 75% and 50.0, respectively. To detect PD‐D, the optimal MTx‐%C, MTx‐RT and MTx‐Cp cutoff were 69%, 1.341s and 40.6, respectively. Conclusion The MemTrax provides rapid, valid and reliable metrics for assessing cognition in patients with PD that could have practical clinical utility for identifying PD‐MCI at early stage and monitoring cognitive function decline during the progression of disease.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".