A Comprehensive Literature Review Of Dysphagia Screening Protocols for Stroke
Bibliographic record
Abstract
Dysphagia is a common complication in acute stroke patients, affecting up to 50% of cases. Timely identification and management of dysphagia are critical to prevent adverse outcomes such as aspiration pneumonia, malnutrition, dehydration, and prolonged hospital stays. This literature review examines the effectiveness and implementation of dysphagia screening protocols for acute stroke patients in various healthcare settings. Key components of dysphagia screening protocols, including timing, tools, and multidisciplinary involvement, are discussed. The review highlights the benefits of using standardized screening tools such as the Bedside Swallowing Assessment and the Toronto Bedside Swallowing Screening Test, which have demonstrated reliability and accuracy in detecting dysphagia. Barriers to effective screening, including lack of trained personnel, inconsistent protocol application, and resource limitations, are also explored. Evidence suggests that early dysphagia screening—ideally within 24 hours of stroke onset—significantly reduces the risk of complications and improves patient outcomes. The integration of dysphagia screening into stroke care pathways and the role of training and education for healthcare professionals are emphasized as critical for successful implementation. This review concludes with recommendations for practice, including adopting validated tools, ensuring timely screening, and fostering interprofessional collaboration to enhance the quality of care for acute stroke patients. Further research is recommended to address gaps in knowledge, particularly concerning the long-term impact of dysphagia screening on patient recovery and healthcare costs.
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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 | 0.009 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".