Rapid communication: Preliminary validation of a telephone adapted Montreal Cognitive Assessment for the identification of mild cognitive impairment in Parkinson’s disease
Bibliographic record
Abstract
Objective: In the current pandemic, tele-screening of neuropsychological status has become a necessity. Instruments developed for telephone screening are not as well validated as traditional neuropsychological measures. Therefore, the current study presents preliminary validation of a telephone version of the Montreal Cognitive Assessment (T-MoCA) in individuals with Parkinson’s disease (PD).Method: Twenty-one persons with PD completed the T-MoCA along with a traditional neuropsychological battery. Diagnostic accuracy for the presence of PD-related mild cognitive impairment (MCI) and correlations with traditional neuropsychological measures are reported.Results: Individuals with MCI (n = 9) scored lower than individuals without cognitive impairment (17.56 vs. 19.50; t = −2.28, p = .03, d = –1.00). Diagnostic accuracy for MCI ranged from 76% to 81%, with sensitivity ranging from 0.56 to 0.67 and specificity ranging from 0.92 to 1.00. Correlations of T-MoCA derived scores with traditional neuropsychological measures were quite modest, with the exception of the memory impairment scale.Conclusions: This rapid communication presents preliminary validation of the T-MoCA for use in individuals with PD. Caveats and implications for practical use in the current pandemic 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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".