Discriminability of Mild Cognitive Impairment Subtypes Based on Neuropsychological Test Outcomes from a Memory Clinic in Puerto Rico
Bibliographic record
Abstract
Abstract Objective The aim of the study was to research test outcomes in Dementia Rating Scale-2 (DRS-2; Spanish adapted version), Montreal Cognitive Assessment (MoCA), Mini Mental Status Exam (MMSE), and Geriatric Depression Scale short form (GDS-SF), as predictors of the different Mild Cognitive Impairment (MCI) subtypes. Participants and Method Our sample constituted of 169 total participants (113 females and 56 males), with ages ranging from 44 to 88 (M = 68.20, SD = 9.59). Educational level presented by sample included 67.5% with professional degrees, 21.9% with a high school diploma, and 10.1% with less than a high school education. We conducted hierarchical logistic regression analysis to generate predicted probabilities of the cognitive tests’ total scores in identifying MCI subtypes. We tested four individual models- each utilized a different MCI subtype (amnestic MCI, single; amnestic MCI, multiple; non-amnestic MCI, single; non-amnestic MCI, multiple) as the dependent variable. The MoCA, DRS-2, MMSE, and the GDS-SF total scores were used as predictors in each analysis. Results We found statistical significance in our four regression models: χ2(1) = 46.26, p < .05 for the model with amnestic MCI, multiple; χ2 (1) = 17.62, p < .05 for the model with amnestic MCI, single; χ2(1) = 15.35, p < .05 for the model with non-amnestic MCI, multiple; and χ2(1) = 18.74, p < .05 for the model with non-amnestic MCI, single. Conclusions Overall, the results in this study suggest that the DRS-2 and the MoCA, two relatively brief and comprehensive screening instruments, are able to discriminate between individuals with varying forms of cognitive impairment. Participants in the amnestic subtypes of MCI performed significantly lower on both of these tests. Our results also suggest that MMSE better discriminates for non-amnestic subtypes. Finally, the GDS-SF suggests better discriminability between memory related cognitive impairment and emotionally related cognitive impairment.
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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 | 0.000 |
| 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".