Cognitive impairment in Parkinson's Disease using Telephone Montreal Cognitive Assessment version: Preliminary results
Bibliographic record
Abstract
BACKGROUND: Mild cognitive impairment (MCI) and dementia are non-motor symptoms of Parkinson's disease (PD). The Telephone Montreal Cognitive Assessment (T-MoCA) is a quick tool designed to screen for cognitive dysfunction remotely; it allows for clinical decision-making. METHOD: Cross-sectional study carried out during 2022-2024 in Lima Peru. Eligible subjects were screened remotely by phone using T-MoCA, Geriatric depression scale (GDS) and, and Pfeffer functional activities questionnaire. The T-MoCA scores for each group were: cognitively unimpaired (18-22), MCI (15-17) and dementia (<15). Groups were compared using ANOVA, and a Spearman correlation matrix was used to explore relationships between sociodemographic and cognitive variables. Ethical approval was obtained by CIEI-INCN. RESULT: We screened 155 PD cases, 60.7% men. Age at onset were 57.6 ±13.8, age at examination were 63± 12.9. The average of year of education of 11 [6-14]. We divided the patients into 3 groups: cognitively unimpaired (n = 42), MCI (n = 40), and dementia (n = 73); and the T-MoCA average score was 19.4± 1.3, 16.1 ± 0.74, and 10.1 ±3.14, respectively. No differences were found between the groups by sex. The GDS average score was 6.6±3.8 points suggesting mild depression. Years of education positively correlated with sentences repetition, abstraction, and the total T-MoCA score (r = 0.56, r=0.63 and r = 0.65, respectively). The Pfeffer Score showed a negative correlation with the T-MoCA (r = -0.54), reflecting an increase in functional dependence in patients with greater impairment. CONCLUSION: The T-MoCA is a remote tool able to differentiate degrees of cognitive impairment in PD and emphasize the importance of sociodemographic factors, such as years of education, in cognitive performance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".