Assessment and Management of Dysphagia in Acute Stroke: An Initial Service Review of International Practice
Bibliographic record
Abstract
The international approach to the assessment and management of dysphagia in the acute phase post stroke is little studied. A questionnaire was sent to clinicians in stroke services that explored the current practice in dysphagia screening, assessment, and management within the acute phase post stroke. The findings from four (the UK, the US, Canada, and Australia) of the 22 countries returning data are analysed. Consistent approaches to dysphagia screening and the modification of food and liquid were identified across all four countries. The timing of videofluoroscopy (VFS) assessment was significantly different, with the US utilising this assessment earlier post stroke. Compensatory and Postural techniques were employed significantly more by Canada and the US than the UK and Australia. Only food and fluid modification, tongue exercises, effortful swallow and chin down/tuck were employed by more than fifty percent of all respondents. The techniques used for assessment and management tended to be similar within, but not between, countries. Relationships were found between the use of instrumental assessment and the compensatory management techniques that were employed. The variation in practice that was found, may reflect the lack of an available robust evidence base to develop care pathways and identify the best practice. Further investigation and identification of the impact on dysphagia outcome is needed.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".