Abstract TP392: Cracking the Code, a Multidisciplinary Approach to Safe, Effective, and Evidence Based Bedside Dysphagia Screening of Stroke Patients With Three Textures
Bibliographic record
Abstract
Background: Within the first three days of a stroke, 42-57% patients present with dysphagia characterized by difficulty swallowing, aspiration, and silent aspiration. Early dysphagia screening is commonly completed using a variety of screening tools, to prevent dysphagia related adverse health consequences and decrease NPO time. Hypothesis: The current study proposes that implementing a specific and effective dysphagia screening tool will result in early identification of dysphagia, reduced duration of NPO status, and prevent dysphagia related health consequences. Methods: A multidisciplinary team compared literature against our current screening process with water and applesauce in non-specific amounts. A new standard of care for dysphagia screening with a distinct algorithm for decision-making was developed. The algorithm established screening with three general oral intake consistencies including three ounces of water, three teaspoons applesauce and half piece of a graham-cracker. Instructions guiding the practicing nurse were created in the EMR and initiated in Q1 of 2015. Simultaneously, 100% practitioner training was completed by speech pathologists and clinicians. A quality report to monitor compliance with new standard of care and outcomes was recently developed. Results: Literature investigation on dysphagia screening in stroke patients yielded the importance of the establishment of a swallow screening tool in patients who exhibit stroke and stroke-like symptoms and in patients reporting or suspected to have dysphagia in general. One of the most common practices is the water swallow screen, which screens patients for safe tolerance of thin liquids. However, in implementation, eludes to patients ability to safely tolerate regular diet consistencies. Based on the significant differences in swallow biomechanics for liquid vs. solid consistencies, we determined the importance of screening the patients swallowing ability beyond passing a screen with thin liquids. Conclusions: The new revised, three texture dysphagia screen improves the ability to identify dysphagia earlier and reduce the duration of NPO status. A validation data collection to support these hypotheses is underway.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".