Abstract TP326: Effect of Dysphagia Screening Strategies on Clinical Outcomes After Stroke a Systematic Review for the 2018 AHA/ASA Guidelines for the Early Management of Patients With Acute Ischemic Stroke
Bibliographic record
Abstract
Introduction: Dysphagia screening protocols have been recommended to identify patients at risk for aspiration. The American Heart Association convened an Evidence Review Committee to systematically review evidence for the effectiveness of dysphagia screening protocols to reduce the risk of pneumonia, death, or dependency after stroke. Methods: The Medline, Embase, and Cochrane databases were searched on November 1, 2016, to identify randomized controlled trials (RCTs) comparing dysphagia screening protocols or quality interventions to increased dysphagia screening rates, and reporting outcomes of pneumonia, death or dependency. Results: Three RCTs were identified. One RCT (n=1,126) found that a combined nursing quality improvement intervention targeting fever and glucose management as well as dysphagia screening reduced death and dependency (42% vs. 58%, p=0.002), but without reducing the pneumonia rate (2.1% vs. 2.7%, p=0.82). Another RCT (n=311) failed to find evidence that pneumonia rates were reduced by adding the cough reflex test to routine dysphagia screening (26% vs. 21%, p=0.38). A smaller RCT (n=162) randomly assigned 2 hospital wards to a stroke care pathway including dysphagia screening or regular care, and found that patients on the stroke care pathway were less likely to require intubation and mechanical ventilation (7.8% vs. 20%, p=0.03 after adjustment); however, the study was small and at risk for bias. Conclusions: There were insufficient RCT data to determine the effect of dysphagia screening protocols on reducing rates of pneumonia, death, or dependency after stroke. Additional trials are needed to compare the validity, feasibility, and clinical effectiveness of different screening methods 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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".