MRI-Based Neuroanatomical Predictors of Dysphagia after Acute Ischemic Stroke: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Considering that the incidence of dysphagia is as high as 55% following acute stroke, we undertook a systematic review of the literature to identify lesion sites that predict its presence after acute ischemic stroke. METHODS: We searched 14 databases, 17 journals, 3 conference proceedings and the grey literature using the Cochrane Stroke Group search strategy and terms for MRI and dysphagia. We evaluated study quality using the Cochrane Collaboration's risk of bias tool and extracted individual-level data. We calculated relative risks in order to model dysphagia according to neuroanatomical lesion sites. RESULTS: Of 964 abstracts, 84 articles met the criteria for full review. Of these 84 articles, 17 met the quality criteria. These 17 articles dealt exclusively with dysphagia after infratentorial stroke and provided MRI correlates of dysphagia for 656 patients. The incidence of dysphagia according to stroke region was 0% in the cerebellum, 6% in the midbrain, 43% in the pons, 40% in the medial medulla and 57% in the lateral medulla. Within these regions, pontine (relative risk 3.7, 95% confidence interval 1.5-7.7), medial medullary (relative risk 6.9, 95% confidence interval 3.4-10.9) and lateral medullary lesions (relative risk 9.6, 95% confidence interval 5.9-12.8) predicted an increased risk of dysphagia. CONCLUSIONS: We sought to develop a neuroanatomical model of dysphagia throughout the whole brain. However, the literature that met our quality criteria addressed the MRI correlates of dysphagia exclusively within the infratentorium. Although not surprising, these findings are a first step toward establishing a neuroanatomical model of dysphagia after infratentorial ischemic stroke and provide insight into the assessment of individuals at risk for dysphagia.
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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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".