Dysphagia screening tools for acute stroke patients available for nurses: A systematic review
Bibliographic record
Abstract
Background & Aim: There is a high incidence of dysphagia after stroke that, depending on the assessment, methodology and time elapsed, can range from 8.1% to 80%. Early and systemic dysphagia screening is associated with a decreased risk of aspiration pneumonia and prevents inadequate hydration/nutrition. The purpose of this systematic review was to identify dysphagia screening tools for acute stroke patients available for nurses validated against reference test. The research question was: which dysphagia screening tools for acute stroke patients available for nurses? Methods & Materials: Three electronic databases were searched from January 2007 to November 2017: on PubMed, Scielo and CINAHL Plus. Two independent reviewers screened all titles and abstracts, assessed methodological quality and extracted data. The methodological quality analysis and evaluation was guided according to four domains: patient selection, index test, reference standard and flow and timing. Divergences between reviewers in data extraction were consensualized through discussion. Results: From the 377 articles retrieved, only three articles met criteria for review: Barnes-Jewish Hospital-Stroke Dysphagia Screen; the Gugging Swallowing Screen and, The Toronto Bedside Swallowing Screening Test. None of the screening tools complies with all psychometric properties, which means that a still significant proportion of patients will be kept nil by mouth without being necessary or that some patients will “fall through the cracks” interrupting the diagnostic process. The tools identified are different from each other, making their comparison impracticable. Conclusion: Due to psychometric proprieties and dietary recommendations adjusted to dysphagia severity, of all available tools, GUSS is a suitable screening tool for nurses in clinical practice.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".