P.066 Failing a dysphagia screen after acute ischemic stroke is highly predictive of poor outcomes
Bibliographic record
Abstract
Background: Bedside dysphagia screening is recommended for all patients with acute ischemic stroke, in order to detect swallowing impairment early and prevent complications. However, limited data are available on outcomes associated with failing a dysphagia screen. Methods: We used the Ontario Stroke Registry to identify patients who were admitted to Regional Stroke Centres from 2010-2013 and received a dysphagia screen within 72 hours. We used multivariable regression to determine outcomes of patients who failed the dysphagia screen. Results: Among 5145 patients who underwent dysphagia screening, 2458 (47.8%) failed and 2687 (52.2%) passed. Patients who failed had more co-morbidities and presented with more severe strokes (mean NIHSS 11.0 vs. 5.4). Among those who failed, 9% required permanent feeding tubes, versus 0.1% among those who passed. After controlling for age, co-morbidities, and stroke severity, failing a bedside swallowing screen remained highly predictive of poor outcomes, including decubitus ulcer (adjusted odds ratio aOR 10.5), pneumonia (aOR 4.6), discharge to long-term care (aOR 4.1) and 30-day mortality (aOR 4.5; 16.6% vs. 2.2%). *All p <0.0001 Conclusions: Patients who failed a dysphagia screen on admission had dramatically worse outcomes after controlling for baseline factors. A bedside dysphagia screen provides immediate risk stratification for acute stroke patients and can be used to guide appropriate care.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".