The Language and Memory Test: Multinational Feasibility Study of a Digital Test to Measure Cognitive Change
Bibliographic record
Abstract
Cognitive measures that are brief, tolerable, accurate, and inexpensive permit mechanistic insights, support clinical trials, and facilitate clinical care. The Language and Memory Test (LMT) is a novel digital test measuring rapid naming (Language 1 and 2), visuospatial memory (Memory 1 and 2), and fine motor dexterity (Finger tapping). The LMT takes approximately 4 min to complete and was designed to be cross-culturally acceptable. This feasibility study of the LMT in English and non-English speaking adults in three countries employed in-person and remote, supervised and unsupervised administration modalities. We investigated test-retest reliability, internal consistency, tolerability, equivalence across centers, administration modalities, language spoken, and age groups. A total of 440 adults ages 18-84 years in Canada, Iran, and the United States, a control sample (n = 115) and neurologic populations (n = 325), completed the LMT. Tolerability was good: 99% who began the test completed all subtests; 115 participants from the United States and Canada samples completed the LMT twice. Test-retest reliability ranged from medium to large: finger tapping (r = 0.892), Language 1 (r = 0.697), Language 2 (r = 0.687), Memory 1 (r = 0.433), and Memory 2 (r = 0.265). Equivalence varied across centers, administration modalities, and language status. Age-related performance decrements were shown for all subtests. Results support the feasibility of the LMT as a brief, multidomain tool for use in healthy adults and neurologic populations and in both in-person and remote settings. Its use across cultures remains to be validated in rigorous studies using culturally adapted versions of the LMT. Sensitivity to change over time remains to be established by future research.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".