Database on Cognitive Impairment in Ecuadorian Adults: Application of the MoCA Test in its Mobile Version with Sociodemographic, Clinical, and Behavioral Variables
Bibliographic record
Abstract
Database Description The database contains information on 1203 records documenting the application of the MoCA (Montreal Cognitive Assessment) test in Ecuadorian adults, with the aim of assessing cognitive impairment and analyzing its relationship with various sociodemographic, clinical and behavioral variables. The main characteristics are described below: Variables Included The database is structured in the following columns: Occupation: Participants' employment classification (e.g., "Full-time employee", "Part-time employee"). Sex: Participant's gender (Male/Female). History: Relevant medical history information (e.g., hypertension, diabetes, psychiatric disorders, etc.). Residence: Type of place where the participant lives (Urban/Rural). Alcohol: Frequency of alcohol consumption (Never, Occasionally, Frequently). Tobacco: Frequency of tobacco use (Never, Occasional smoker, Ex-smoker). Physical activity: Level of physical activity (Sedentary, Light, Moderate, Intense). Years: Years of study completed by the participant. Time_minutes_seconds: Duration in minutes and seconds it took to complete the MoCA test. Cog_num: Numerical score obtained in the MoCA Test. Cog_ord: Ordinal rating of cognitive performance ("Performance Below Expected", "Performance Slightly Below Expected", etc.). Purpose The main purpose of this database is: To assess cognitive impairment in Ecuadorian adults using the MoCA Test. To analyze how sociodemographic (age, gender, occupation), clinical (medical history) and behavioral (physical activity, tobacco and alcohol consumption) factors are related to cognitive outcomes. Salient Features The database includes a wide variety of data that allow for descriptive and correlational analyses. The data were collected using the mobile version of the MoCA Test in Spanish, which ensures accuracy and standardization. The variable "Time_minutes_seconds" provides information on the duration of the test for each participant. Example Records A typical example in the database is: Possible Uses Identify patterns between cognitive impairment and variables such as physical activity or medical history. To develop strategies for the prevention of cognitive decline in specific populations. Generate scientific articles based on the data collected.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".