LAS‐Face‐ Name Associative Memory Exam: Discriminant validity for the detection of associative memory disorders in people with mild cognitive impairment in Argentina
Bibliographic record
Abstract
Abstract Background The Latin American Spanish version of the Face‐Name Associative Memory Exam (LAS‐FNAME) is a validated version of the test that has been used to detect subtle cognitive changes in cognitively unimpaired adults at increased risk for Alzheimer’s disease (AD). Performance in this test has also been associated with markers of brain pathology in cognitively unimpaired older adults at increased risk for AD due to genetic factors or subjective cognitive decline (Papp et al., 2014;Vila‐Castelar et al., 2020). However, this test has not yet been used for the detection of Mild Cognitive Impairment (MCI) in the Latin American population. This study aims to analyze the discriminative validity and diagnostic performance of the LAS FNAME for the detection of verbal associative memory disorders in patients with MCI. Method We recruited 30 patients with MCI according to Petersen’s criteria (2010) and 20 healthy controls matched by age, sex and educational level. All participants completed the LAS‐FNAME, which consists of 12 learning trials of novel pairs of face‐names (immediate learning), followed by a delayed recall condition, and a neuropsychological assessment that included: Montreal Cognitive Assessment (MoCA), Craft Story 21, Multifactorial Memory Questionnaire (MMQ‐S) and a Functional Activity Questionnaire (FAQ). Result The groups did not differ in age, sex or education (p = 0.25, p = 0.54, p = 0.54). The age range was 61 to 86 years (M = 74.66; SD = 6.65) and the education range was 7 to 18 years (M = 14.63; SD = 3.41). The LAS‐FNAME showed an ability to discriminate against healthy controls from patients with MCI, both in its immediate (AUC = 0.81) and delayed (AUC = 0.79) conditions. The predictive ability was superior than a screening measure (MoCA, AUC = 0.74). The sensitivity and specificity was 68.4% and 89.3%, respectively. The LAS‐FNAME also showed evidence of concurrent validity with a standard memory test (Craft Story 21) in both immediate (r = 0.52, p<0.01) and delayed (r = 0.63, p<0.01) conditions, and reliability was excellent (α = 0.91). Conclusion Performance on the LAS‐FNAME was able to distinguish MCI patients from healthy controls, suggesting that the LAS‐FNAME can detect early cognitive changes in prodromal stages of Alzheimer’s disease in Spanish‐speaking individuals.
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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 | 0.000 |
| 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".