Ischemic Lesion Location Based on the ASPECT Score for Risk Assessment of Neurogenic Dysphagia
Bibliographic record
Abstract
Dysphagia is common in patients with middle cerebral artery (MCA) infarctions and associated with malnutrition, pneumonia, and mortality. Besides bedside screening tools, brain imaging findings may help to timely identify patients with swallowing disorders. We investigated whether the Alberta stroke program early CT score (ASPECTS) allows for the correlation of distinct ischemic lesion patterns with dysphagia. We prospectively examined 113 consecutive patients with acute MCA infarctions. Fiberoptic endoscopic evaluation of swallowing (FEES) was performed within 24 h after admission for validation of dysphagia. Brain imaging (CT or MRI) was rated for ischemic changes according to the ASPECT score. 62 patients (54.9%) had FEES-proven dysphagia. In left hemispheric strokes, the strongest associations between the ASPECTS sectors and dysphagia were found for the lentiform nucleus (odds ratio 0.113 [CI 0.028-0.433; p = 0.001), the insula (0.275 [0.102-0.742]; p = 0.011), and the frontal operculum (0.280 [CI 0.094-0.834]; p = 0.022). A combination of two or even all three of these sectors together increased relative dysphagia frequency up to 100%. For right hemispheric strokes, only non-significant associations were found which were strongest for the insula region. The distribution of early ischemic changes in the MCA territory according to ASPECTS may be used as risk indicator of neurogenic dysphagia in MCA infarction, particularly when the left hemisphere is affected. However, due to the exploratory nature of this research, external validation studies of these findings are warranted in future.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".