Multivariate Base Rates of Low Scores on Tests of Learning and Memory Among Latino Adult Populations
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of low scores for two neuropsychological tests with five total scores that evaluate learning and memory functions. METHOD: N = 5402 healthy adults from 11 countries in Latin America and the commonwealth of Puerto Rico were administered the Rey-Osterrieth Complex Figure (ROCF) and the Hopkins Verbal Learning Test (HVLT-R). Two-thirds of the participants were women, and the average age was 53.5 ± 20.0 years. Z-scores were calculated for ROCF Copy and Memory scores and HVLT-R Total Recall, Delayed Recall, and Recognition scores, adjusting for age, age2, sex, education, and interaction variables if significant for the given country. Each Z-score was converted to a percentile for each of the five subtest scores. Each participant was categorized based on his/her number of low scoring tests in specific percentile cutoff groups (25th, 16th, 10th, 5th, and 2nd). RESULTS: Between 57.3% (El Salvador) and 64.6% (Bolivia) of the sample scored below the 25th percentile on at least one of the five scores. Between 27.1% (El Salvador) and 33.9% (Puerto Rico) scored below the 10th percentile on at least one of the five subtests. Between 5.9% (Chile, El Salvador, Peru) and 10.3% (Argentina) scored below the 2nd percentile on at least one of the five scores. CONCLUSIONS: Results are consistent with other studies that found that low scores are common when multiple neuropsychological outcomes are evaluated in healthy individuals. Clinicians should consider the higher probability of low scores when evaluating learning and memory using various sets of scores to reduce false-positive diagnoses of cognitive deficits.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".