Effects of Expiratory Muscle Strength Training on Swallowing in Survivors of Critical Illness: A Protocol for a Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Background and Aims: Post Extubation Dysphagia (PED) is a common consequence of mechanical ventilation. Muscular weakness and atrophy are potential causes. Expiratory Muscle Strength Training (EMST) is a technique whereby a subject exhales against a resistance, strengthening the muscles of expiration. There is evidence that EMST causes activation and hypertrophy of the muscles of swallowing, with clinical evidence that it improves swallowing in certain populations. The aim of this systematic review is to collate the existing literature concerning evaluation of swallowing after extubation, and whether EMST positively affects these measures. Methods: We will perform a systematic review of the literature by searching electronic databases (Pubmed, Medline, EMBASE, and the Cochrane Library), for articles where EMST has been performed (alone or in conjunction with inspiratory muscle training), in patients who have been liberated from a period of mechanical ventilation. We will identify studies that evaluate swallowing after extubation, listing the methods used to evaluate swallowing and data will be extracted from studies evaluating the impact EMST has on these measures. Results: We will undertake meta-analysis if data permits. Risk of bias will be assessed using the Risk of Bias 2 tool or the Newcastle Ottawa Score for randomized and non-randomized trials. We will use The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to assess the quality of evidence. Conclusion: : 42023444479.
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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 | 0.057 | 0.084 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.041 | 0.004 |
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".