Montreal Cognitive Assessment (MoCA): normas para la población del área metropolitana de Rosario, Argentina
Bibliographic record
Abstract
INTRODUCTION: Population aging is a global phenomenon linked to increased life expectancy. In Argentina, it is expected that by 2025 those over 60 will represent 17.3% of the population, while by 2050 it will rise to 25.3%. Among the pathologies associated with aging, cognitive impairment and dementias represent an important problem for public health and demand effective instruments for their early detection. OBJECTIVE: Obtain normative data for the Montreal Cognitive Assessment (MoCA) in Argentine adults and seniors in the Rosario Metropolitan Area. SUBJECTS AND METHODS: The MoCA-Spanish version was administered according to the instructions published in the original version. An ad hoc survey was also administered to collect sociodemographic information and medical history. The influence of some sociodemographic variables on performance was analyzed. 225 adult residents of the Rosario Metropolitan Area participated in the final sample (age: M = 66.1, standard deviation = 8.7). RESULTS: Educational level predicted 13% of the variance of the total MoCA score, -F (3, 221) = 12.11; p < 0.01-. Other variables considered, such as age and sex, were not significant for predicting the score. CONCLUSION: The normative data obtained suggest a cut-off point of 18 for people with primary education and of 22 for people with secondary or higher education. It should be noted that they are below those indicated in the pre-existing regulatory data. The importance of using norms adjusted to the sociocultural context is highlighted.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".