Correlation of Performance on the Montreal Cognitive Assessment (MoCA) and Generative Semantic Subtests of the EFA-4 (Examining for Aphasia-4) in Persons with Memory and/or Communication Difficulty
Bibliographic record
Abstract
Purpose: To examine the relationship between scores on the Generative Semantic Naming Subtests of the EFA-4 and the MoCA in persons with memory and/or communication difficulty. Methods: Six adults with varying diagnoses of memory and/or communication difficulty were included. The entire Montreal Cognitive Assessment (MoCA) and the four Generative Semantic Naming Subtests of the EFA-4 (Examining for Aphasia- 4) were administered. Scores were correlated using Pearson r Correlational Analyses. Results: We found strong, positive correlations between the MoCA and overall EFA-4 raw scores and also between the MoCA and each individual subtest. These correlations confirm that there is a predictable relationship in performance on these two tests. Conclusions: Strong, positive correlations in performance indicates a predictable relationship in performance on the MoCA and EFA-4. The correlations also demonstrate a relationship between lexical-semantic processing and cognitive decline. The degree or severity of dementia, as measured by MoCA scores, affects word retrieval and naming in a predictable manner.
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 | 0.007 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".