A-42 * Utility of the Montreal Cognitive Assessment (MoCA) Spanish Version in Detecting Cognitive Impairment in a Puerto Rican Temporal Lobe Epilepsy (TLE) Sample
Bibliographic record
Abstract
Objective: The current study investigated the utility of the MoCA in screening for cognitive impairment and detecting memory deficits in a Puerto Rican sample diagnosed with Temporal Lobe Epilepsy (TLE) showing no significant limitations on their Instrumental Activities of Daily Living (IADl's). Method: Data were obtained from 24 TLE patients (69.6 % females and 30.4 % males, mean age = 49) referred by their neurologists for outpatient neuropsychological testing. Patients were independent in their IADL's showing below cut offs on a standardized functional capacity measure (FAQ mean of 4.59). Results: MoCA mean score was 24.8 (± 3.53), which represented below cutoffs scores for cognitive impairment. MMSE mean score was 26.2. In spite of normal MMSE scores (86.0% of the participants obtained MMSE Score > 25), cognitive impairment was detected in 62.5% of the patients. MMSE detected only 13.0%. Also, there were significant and positive correlations between the MoCA and three memory measures derived from the neuropsychological evaluation (Learning, Immediate recall and Delayed recall measures). MoCA total score correlates moderately significant with the RAVLT Total Learning (r = .496, p = .016); Logical Memory I (r = .438, p = .032); Logical Memory II (r = .630, p = .001) and Visual Memory II (r = .413, p = .050). A significant moderate and inverse relation (r = −.574, p = .017) correlated with the FAQ. Conclusion(s): The results obtained in the present study support utility of the MoCA in screening for cognitive impairment and detecting memory deficits in a Puerto Rican sample diagnosed with Temporal Lobe Epilepsy (TLE) showing no significant limitations on their Instrumental Activities of Daily Living (IADl's).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".