Which screening tool should be used for identifying aspiration risk associated with dysphagia in acute stroke? A Cochrane Review summary with commentary
Bibliographic record
Abstract
BACKGROUND: Dysphagia is a common impairment in patients with acute stroke and is associated with an increased risk of complications such as aspiration pneumonia, malnutrition and dehydration, as well as with poor outcome and higher mortality. Therefore, immediate screening for aspiration risk is recommended, using a bedside swallow screening tool. OBJECTIVE: To determine the diagnostic accuracy and the sensitivity and specificity of bedside screening tests for detecting risk of aspiration associated with dysphagia in people with acute stroke. METHODS: A summary of the Cochrane Review by Boaden et al. 2021, with comments from a rehabilitation perspective. RESULTS: The review included 25 studies with 3953 participants and 37 screening tests. No single study demonstrated 100% sensitivity and specificity with low risk of bias for all domains. The best performing swallow screening tools were the Bedside Aspiration test (combined water swallow and instrumental tool), the Gugging Swallowing Screen (GUSS, water plus other consistencies) and the Toronto Bedside Swallowing Screening Test (TOR-BSST, water only). However, these tests were based on single studies with small sample sizes. It was not possible to explore the influence of sources of heterogeneity. CONCLUSIONS: No single swallow screening tool with high accuracy as well as good quality evidence could be identified, but recommendations for further high-quality research are offered.
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".