Nurse’s bedside screening of dysphagia: an umbrella review
Bibliographic record
Abstract
INTRODUCTION: Dysphagia often results in serious, poor health outcomes. Nurses have an important role in assessing dysphagia. Therefore, they need reliable and effective screening tools to detect dysphagia. The purpose of this umbrella review is to locate the most valid, reliable, and usable bedside screening tools that allow nurses to identify dysphagia in institutionalized patients. METHODS: Umbrella Review as suggested by the Joanna Briggs Institute. Inclusion criteria were: systematic reviews of randomized controlled trials or cross-sectional studies. We excluded: pediatric and psychiatric patients. We searched on PubMed, CINAHL, Scopus, Cochrane Library, the Joanna Briggs Institute Database of Systematic Reviews and Implementation Reports, and the Joanna Briggs Institute Evidence-Based Practice Database. RESULTS: Six reviews were included. Four tools were reported in all the reviews: 3 oz swallowing water test, Mann Assessment of Swallowing Ability, Toronto Bedside Swallowing Screening Test, Gugging Swallowing Screen. They have shown fair to good sensitivity and specificity. The reviews analysed did not allow for a comparative analysis between instruments, which may be hindering the selection of the optimal instrument for clinical practice. CONCLUSIONS: Almost all reviews have considered stroke patients. The next steps will be to determine if there is a tool applicable in multiple settings with different patients and if this intervention is cost-effective.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".