A-63 * Standardization Project: Montreal Cognitive Assessment and Semantic Verbal Fluency in Puerto Rican Adults Aged 50 to 90 Years
Bibliographic record
Abstract
Objective: In Puerto Rico, there exists a paucity of neuropsychological tests with normative data to make an accurate and appropriate diagnosis to the population. Our aim is to collect normative data from the Montreal Cognitive Assessment (MoCA) and Semantic Verbal Fluency (SVF) on a Puerto Rican non-clinical adult population between 50–90 years old. This presentation includes preliminary data of the ongoing study. Method: Phase I includes a rule out of cognitive impairment: family member interview, Clock Drawing Test & Phonemic Verbal Fluency. Phase II include the experimental measures: MoCA & SVF. Quantitative, non-cross experimental descriptive design was analyze with a preliminary sample (n = 27). The average age was 57 years (SD = 5.566), most with Bachelor Degree education (SD = .76), and 55.6% were females. All the participants were Puerto Rican adults with 50–71 years old, selected by availability. Results: ANOVA was performed with the MoCA relative to age and schooling variables. No significant differences were found. SVF related to named animals and supermarket items reflected a significant deference in animal and academic degrees, while grocery items showed no significant difference, a large animal eta2 of .31 and a median of .13 for grocery items. Conclusion(s): Preliminary results with the MoCA showed that older participants reflect lower scores. Similarly, it was shown that individuals with less education tend to produce fewer animal names. This trend will be evaluated with a bigger sample as part of the final normative study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".