Abstract P63: Post-Stroke Cognitive Complaints and Normal MoCA Scores: A Possible Role for Machine Learning-Augmented Cognitive Screening
Bibliographic record
Abstract
Background: The Montreal Cognitive Assessment (MoCA) may be insufficiently sensitive to cognitive impairment in high-functioning stroke survivors. We examined the ability of a machine learning (ML) algorithm to distinguish young stroke survivors with mRS of 0-1 and MoCA >26 vs. age-matched controls. Methods: As part of a study comparing performance of the NIH Toolbox Cognitive Battery (NIHTB-CB) in characterizing cognitive deficits in young survivors, we assessed 52 survivors and 53 healthy controls. Voice recordings of subjects describing the Cookie Theft photo were analyzed using a Natural Language Processing algorithm incorporating 335 lexical and acoustic features. We used a stratified five-fold cross validation, performing a feature selection step before training where we selected for inclusion into the model the first k features with the highest absolute correlation with labels in the training fold. Area under the receiver operating curve (AUC) was calculated for each model. Results: MoCA was >26 in 83% of controls and 63% of stroke survivors. Using a Gaussian Naive Bayes Classifier, AUC for stroke survivors vs. controls in those with MoCA >26 was 0.74 (95%CI 0.58-0.91). Informative non-acoustic features included lexical and syntactic complexity, info-units (ie. features in the picture) and spatial orientation of info-units. The analysis is ongoing and relation with NIHTB-CB scores will be reported subsequently. Conclusions: There is a ceiling effect of the MoCA. Automated ML assessments may serve as efficient screening adjuncts prior to more detailed cognitive assessments in high-functioning stroke survivors with cognitive complaints. Further work is needed.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".