Abstract TP413: Prediction of Dysphagia Using the <i>Alberta Stroke Program Early CT Score</i>
Bibliographic record
Abstract
Background: Dysphagia is common in patients with acute middle cerebral artery (MCA) stroke, and associated with malnutrition, pneumonia and mortality. Hence, identification of patients with swallowing disorder early after stroke onset is important. Besides bedside screening tools, brain imaging findings including lesion size and location may be of value. We investigated whether The Alberta stroke program early CT score (ASPECTS) can be used to predict dysphagia, and whether differences exist herein between the left and the right hemisphere. Methods: The analysis was based on a prospective dataset of 113 patients with acute ischemic stroke in the MCA territory. 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. Results: 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 (ExpB 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). For right hemispheric strokes, only non-significant associations were found which were strongest for the insula region (0.385 [0.107-1.384]; p=0.144). For the left hemisphere multivariate logistic regression analysis revealed lower ASPECT scores to be independently associated with dysphagia, whereas for the right hemisphere this association was not present. Conclusion: The distribution and extent of early ischemic changes in brain imaging according to ASPECTS allows a reliable prediction of dysphagia in MCA-stroke patients, particularly for the left hemisphere.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 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".