Implementation of a swallow screening protocol in a tertiary adult intensive care unit: A quality improvement project
Bibliographic record
Abstract
Background: Post-extubation dysphagia occurs in 3% to 62% of adults who have received invasive mechanical ventilation in an intensive care unit (ICU). A stepwise approach to identify dysphagia includes a routine swallow screening in patients who are recently extubated followed by a formal assessment by a Speech and Language Pathologist (SLP), in the event of a failed swallow screen, has been suggested. This quality improvement project aimed to implement and evaluate a new post-extubation swallow screening process. Methods: Using the Model for Improvement framework, a new process that included the Yale Swallow Protocol (YSP) was developed, implemented, and evaluated between July and December 2022 in a 20-bed tertiary ICU. All patients with at least four hours after post-extubation were included in the project. Process development included a literature review and feedback from ICU educators, nurses, physicians, SLPs, and Registered Dietitians (RD). Education was provided to critical care nurses prior to implementation. Data on patient characteristics, process indicators, and outcomes were collected between September and December 2022 to evaluate the project implementation. Descriptive statistics were used to describe the data and bivariate analyses were used to compare data between patients who passed and failed the YSP. Results: Fifty-four patients were extubated and evaluated using the new swallow screening process. Post-extubation dysphagia was diagnosed by an SLP in 30.0% of patients. Dysphagia was diagnosed in 90.0% of patients who failed compared to 8.6% of patients who passed the YSP (P<0.001). Conclusion: A swallow screening process using the YSP was successfully adopted in a tertiary ICU.
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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 | 0.077 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".