The MoCA ‐ Language (MoCA‐L): A brief screening tool for language disorders in neurodegenerative diseases
Bibliographic record
Abstract
BACKGROUND: Commonly used screening tools such as the Mini-Mental State Examination and the Montreal Cognitive Assessment (MoCA) examine global cognition but lack a specific focus on language functions. To address this gap, we developed the MoCA - Language (MoCA-L) to provide a brief screening tool for cognitive-linguistic disorders such as primary progressive aphasia (PPA) and other neurodegenerative diseases. METHOD: A pilot study (Phase 1) was conducted with 15 healthy controls (7M/8F; mean age = 68.5 years old; mean education = 14.4 years) using a preliminary version of the test. This version comprised a comprehensive set of tasks and items designed to evaluate key speech and language functions functions in PPA and other neurodegenerative diseases (i.e. spontaneous speech, naming, semantics, repetition, sentence comprehension, syntax and grammar). RESULT: Mean and standard deviations were calculated for each item of every task. A consensus-based process within our team of experts was then conducted. Items with higher difficulty (success rate < 90%) were eliminated. Then, the items included in the final version were chosen according to psycholinguistic parameters of interest. The test was narrowed down to a final version comprising six subtests: conversation (/3), picture naming (/8), semantic knowledge (/6), pseudowords and sentence repetition (/7), sentence comprehension (/4) and a picture description (/2), for a total of 30 points. CONCLUSION: The MoCA-L, a brief 6-subtests' screening tool has been developed to improve the detection of language impairments associated with cognitive-linguistic disorders such as PPA and other neurodegenerative diseases. In Phase 2, we plan to establish the psychometric qualities of the test, such as test-retest fidelity, inter-rater reliability, internal consistency and convergent validity. Phase 3 will focus on establishing normative data. Future plans also include the development of an interpretation guide as well as the adaptation of the MoCA-L in various languages and cultures. The MoCA-L will ultimately provide healthcare professionals with a simple and rapid screening tool designed specifically to assess language deficits in neurocognitive disorders, thus improving early diagnosis and management of these diseases.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".