Do stroke clinical practice guideline recommendations for the intervention of thickened liquids for aspiration support evidence based decision making? A systemetic review and narrative synthesis
Bibliographic record
Abstract
Background\nAspiration is a common sequela post stroke as a result of oropharyngeal dysphagia. It is primarily managed by recommending thickened liquids whereby a thickening agent is added to liquids to increase viscosity and slow bolus speed. However, the evidence supporting this intervention is not strong and clinicians may rely on tools such as clinical practice guidelines to support clinical decision-making. The purpose of this systematic review and narrative synthesis was to evaluate the evidentiary bases of recommendations made by clinical guidelines regarding the thickened liquids intervention. \nMethods\nA systematic review was conducted on stroke clinical guidelines retrieved via searches conducted across a range of databases including Academic Search Complete, CINAHL, MEDLINE and the Cochrane Library as well as through association websites. Guidelines were eligible for inclusion if they focused on adult stroke populations, made recommendations relating to the intervention of thickened liquids and were published after January 2010 and before December 2018. Four independent reviewers rated the methodological quality of the guidelines using the AGREE-II instrument. Intervention recommendations were extracted and analysed using the Criteria for Levels of Evidence Reported from the Canadian Stroke Best Practice Recommendations and a framework examining the evidence used by guidelines to support intervention recommendations.\nResults\nThirteen clinical guidelines were included in the review. The methodological quality of included guidelines was variable but generally good-excellent overall. Thirty recommendations regarding the intervention were extracted. Of these, 16 recommendations were classed as a recommendation to use the treatment and all guidelines made this recommendation. Much of the evidence used to scaffold recommendations did not directly support the intervention and the evidence used was inconsistent across recommendations. \nConclusions\nDespite the limited evidence base for the thickened liquid intervention, there was consensus evident among stroke guidelines in recommending it. This is despite limited empirical support. Further, much of the evidence used to support recommendations was not appropriate, suggesting less than satisfactory evidence-based practices in formulating recommendations. In this case, clinical guidelines may not be reliable decision-support tools for facilitating clinical decision-making.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".