[Validation of the Montreal Cognitive Assessment (MoCA) as a screening tool for mild cognitive impairment in the population of Buenos Aires, Argentina].
Bibliographic record
Abstract
INTRODUCTION: The Montreal Cognitive Assessment (MoCA) test is a brief tool for neuropsychological assessment. OBJECTIVE: to validate the MoCAin the population of Buenos Aires, Argentina, to allow for the use of the test for the detection of Mild Cognitive Impairment (MCI). METHODS: The sample consisted of 269 adults over 60 years old and of schooling of more than 6 years (healthy adults n = 115 and MCIn = 154). Receiver operating characteristic (ROC) analysis was used to establish the relationship between the diagnoses of the patients and the scores obtained at MoCA. The optimal cut-off points were selected, and the positive and negative predictive value were calculated for them. RESULTS: The area under the curve (AUC) was 0,741 (p <0001, 95% CI:.682 -.800) for the MMSE and 0.810 (p <0001, 95% CI:.759 -. 861) for the MoCA test. The cut point suggested using the MoCA test is 26 points, which throws .727 of sensitivity and a specificity of. 748. CONCLUSION: The MoCA test is a useful test for clinical consultation. Its brevity and simplicity place it as an interesting instrument for neuropsychological screening in the Argentinian population.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".