Presentation of three French screening tools to detect the lexico-semantic breakdown in mild cognitive impairment and Alzheimer's disease
Bibliographic record
Abstract
Lexico-semantic difficulties are amongst the first symptoms of Alzheimer's disease (AD). Lexical impairment manifests itself through word retrieval difficulties while semantic disorder impairs general knowledge. This profile already occurs in mild cognitive impairment (MCI), constituting a predictive feature of conversion in AD. The aim of this study is to present three tools to rapidly screen lexical and semantic disorder. 97 participants allocated in 3 groups contributed to this Belgian-Quebec study: one group of MCI (N=35, MOCA for French-Canadian =24.18+/-2.3 ; MMSE for Belgian =26.71+/-1.38), one group of AD (N=13, MMSE=24.7+/-2.52) and one control group (N=49, MOCA for French-Canadian=27.79+-1.9 ; MMSE for Belgian=29.07+/-.79). Three tools were administered : 1) the mini Semantic Knowledge Questionnaire (SKQ) composed of 12 multiple choice questions interrogating semantic properties of objects ; 2) The Short Test of naming for Alzheimer's Disease (STN-AD) comprising 11 pictures in black and white to name and 3) the mini-Montreal Semantic Memory Protocol (mini-MSMP) comprising 26 questions interrogating functional or categorical features of objects. The ANOVA indicated that all the three tools (mini-SKQ : F(2 ;94)=8.440 ; p<.001 ; STN-AD : F(2 ;94)=17.760 ; p<.001; mini-MSMP: F(2 ;94)=7.273 ; p=.001) allowed for differentiating our groups. Post hoc tests (Bonferroni) showed that MCI and AD performed poorer than the control group for all three tools (p<.05). The mini-SQK, the STN-AD and the mini-MSMP are three original tools used to evaluate the early lexical and semantic alteration. Their rapid administration made them instrument of choice for an early detection of MCI and AD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".