The development and implementation of an evidence-based risk reduction algorithm for post-extubation dysphagia in intensive care
Bibliographic record
Abstract
Intubation and mechanical ventilation are often required to support critically ill patients. These are life-sustaining measures and when they are no longer necessary, patients need to be carefully transitioned back to breathing, eating, and talking on their own. Post-extubation dysphagia is defined as swallowing difficulty following extubation. This condition can affect up to 87% of critically ill patients and can cause serious health complications such as aspiration pneumonia, which could require re-intubation, prolonged intensive care stays and increased in-hospital mortality. Currently, many extubated patients are trialed with oral intake without dysphagia screening or kept with nothing by mouth pending speech language pathology evaluation. This is not only a source of discomfort and distress for patients, families, and staff but can lead to malnutrition and dehydration, and puts patients at risk for aspiration. Systematically screening extubated patients for dysphagia is an opportunity to improve practice by enabling nurses to advocate for the safe and timely resumption of oral intake. A novel, evidence-based algorithm, called SAPE (Swallowing Algorithm Post-Extubation) was developed by an interdisciplinary critical care team to assist nurses to identify risk factors for post extubation dysphagia and help make evidence-informed decisions regarding referral to speech-language pathology and initiation of per os intake in the absence of a water swallow test. SAPE was implemented in four tertiary-level medical and/or surgical intensive care units. Process and outcome measures of a quality improvement initiative are discussed, and future directions proposed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".