Bruikbaarheid en validiteit van de Nederlandse versie van de Montreal Cognitive Assessment (MoCA-D) bij het diagnosticeren van Mild Cognitive Impairment
Bibliographic record
Abstract
OBJECTIVE: The MoCA is a new screening test to detect Mild Cognitive Impairment (MCI). Purpose of this study is validating the Dutch version (MoCA-D). METHOD: We administered the MoCA-D to healthy control subjects and to elderly with MCI or dementia from a memory disorder outpatient clinic and a geriatric (outpatient) clinic (n = 30, 32, 37 respectively, age > or = 60). Neuropsychological testing was part of the standard procedure for patients to diagnose MCI. Sensitivity, specificity and predictive values (positive: PPV and negative: NPV) of the MoCA-D were assessed. RESULTS: A significant effect of group was found on MoCA-D total score (F (2.95) =67.9; p < 0.01). With a cutoff score of < or = 25, sensitivity and specificity to detect MCI in relation to healthy controls were 72% and 73%, respectively. PPV and NPV were 84% and 56%, respectively. With a cut-off score of < or = 20, sensitivity to detect dementia in relation to MCI was 100% for severe dementia and 75% for mild dementia. Specificity for dementia was 81%, PPV 94% and NPV 55%. CONCLUSION: The MoCA-D distinguishes between healthy elderly, MCI patients and dementia patients. However, in this study, insufficient sensitivity and poor specificity were found. For the present, applying a broader and flexible screening procedure in order to detect MCI seems a more useful method than the interpretation of one test result in particular.
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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 | 0.108 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".