MRI-Based Neuroanatomical Predictors of Dysphagia, Dysarthria, and Aphasia in Patients with First Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: Due to the high post-stroke frequency of dysphagia, dysarthria, and aphasia, we developed comprehensive neuroanatomical, clinical, and demographic models to predict their presence after acute ischemic stroke. METHODS: The sample included 160 randomly selected first-ever stroke patients with confirmed infarction on magnetic resonance imaging from 1 tertiary stroke center. We documented acute lesions within 12 neuroanatomical regions and their associated volumes. Further, we identified concomitant chronic brain disease, including atrophy, white matter hyperintensities, and covert strokes. We developed predictive models using logistic regression with odds ratios (OR) and their 95% confidence intervals (95% CI) including demographic, clinical, and acute and chronic neuroanatomical factors. RESULTS: Predictors of dysphagia included medullary (OR 6.2, 95% CI 1.5-25.8), insular (OR 4.8, 95% CI 2.0-11.8), and pontine (OR 3.6, 95% CI 1.2-10.1) lesions, followed by brain atrophy (OR 3.0, 95% CI 1.04-8.6), internal capsular lesions (OR 2.9, 95% CI 1.2-6.6), and increasing age (OR 1.4, 95% CI 1.1-1.8). Predictors of dysarthria included pontine (OR 7.8, 95% CI 2.7-22.9), insular (OR 4.5, 95% CI 1.8-11.4), and internal capsular (OR 3.6, 95% CI 1.6-7.9) lesions. Predictors of aphasia included left hemisphere insular (OR 34.4, 95% CI 4.2-283.4), thalamic (OR 6.2, 95% CI 1.6-24.4), and cortical middle cerebral artery (OR 4.7, 95% CI 1.5-14.2) lesions. CONCLUSION: Predicting outcomes following acute stroke is important for treatment decisions. Determining the risk of major post-stroke impairments requires consideration of factors beyond lesion localization. Accordingly, we demonstrated interactions between localized and global brain function for dysphagia and elucidated common lesion locations across 3 debilitating impairments. .
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".