Mini‐Linguistic State Examination (MLSE): Preliminary Normative Data for the French‐Canadian Version of an International Screening Tool for Primary Progressive Aphasia
Bibliographic record
Abstract
BACKGROUND: There is a need for internationally applicable screening tests assessing language in neurodegenerative conditions, allowing for homogenized diagnosis/classification of primary progressive aphasias (PPA), monitoring language decline, and providing endpoints in clinical/therapy trials. The 'Mini-Linguistic State Examination' (MLSE) is currently developed within a worldwide network including 22 countries, and our Paris-Québec collaboration aimed at developing the French/Canadian MLSE (fc-MLSE), validating it with healthy controls and clinical populations. METHOD: The fc-MLSE was adapted from the English version using stimuli of similar linguistic complexity. As the English version, the fc-MLSE included 11 sub-tests assessing 5 linguistic domains (motor-speech, phonology, semantics, syntax, verbal working-memory), providing a total score (/100) and 5 sub-scores. The test was administered to 182 healthy controls to generate normative scores, and subsequently to 36 patients with PPA [(nonfluent/agrammatic variant (nfvPPA, n = 8), logopenic variant (lvPPA, n = 20), semantic PPA (svPPA, n = 8)], and 6 patients with Alzheimer's disease (AD). RESULT: Testing durations were approximately 8 and 12 minutes for controls and patients, respectively, and the inter-rater consistency was of 92%. In controls, no ceiling effects were observed. The total score and the 5 sub-scores were similar for women/men, and led to stratifications according to age-ranges and educational levels. In clinical populations, the fc-MLSE was able to distinguish AD and PPA patients from controls, and PPA had lower total MLSE scores than AD patients. Sub-scores also distinguished the three PPA variants, showing highest error-rates for motor speech in nfvPPA, verbal working-memory in lvPPA, and semantics in svPPA. CONCLUSION: The fc-MLSE is a rapid and examiner-consistent language test, validated with a large population of healthy controls. It can be helpful for clinical classification of PPA variants, and might represent a valuable tool for follow-up trial monitoring in PPA, AD and other neurodegenerative diseases affecting language. More generally, the use of MLSE versions developed in 22 countries will improve the consistency/uniformity of language assessments at the international level.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".