Abstract 25: Evidence Based Aspiration Risk Tool: Improving Stroke Care
Bibliographic record
Abstract
Introduction: According to the American Heart Association, approximately 65 percent of stroke patients will have swallowing impairment that needs evaluation prior to oral intake or medications. Per the Joint Commission Guidelines, all stroke patients must be screened for aspiration risk using an evidence-based tool prior oral intake. Hypothesis: We hypothesized that combining the MetroHealth dysphagia screen and 3 oz. water test as a 2-tiered evidence-based screening tool will properly identify stroke patients at risk for aspiration pneumonia. Method: A retrospective cohort analysis of 266 patients was conducted of transient ischemic attack, ischemic, and hemorrhagic stroke patients admitted to a small community hospital from July 2014-June 2015. The hospital implemented a 2-tiered aspiration risk screen (ARS), in addition to standard ARS. Inclusion criteria: stroke patients discharged with additional diagnosis of pneumonitis due to inhalation of food or vomitus (ICD9 507.0). Exclusion criteria: all stroke patients that were discharged without discharge diagnosis of ICD9 507.0. Results: Of the 266 patients, 11 (4.1%) were diagnosed with pneumonitis. Eight patients (72%) were screened for aspiration utilizing both screens. Three of the 8 patients (37.5%) passed the hospital’s standard ARS tool, but were identified to have aspiration risk utilizing the 2-tiered screen. Three of the 11 patients (27%) were identified at risk for aspiration utilizing the 2-tiered ARS only. Two patients were found to have questionable infiltrates on admission. The other patient passed the ARS on admission, and later failed upon rescreen for change in mental status. All patients were evaluated by speech prior to oral intake. Conclusion: This study reveals that the 2-tiered ARS can be utilized to identify stroke patients at risk for aspiration. Despite proper recognition of aspiration risk upon admission, a small number of patients, 4% in our study, will go on to aspirate. An opportunity exists for closer collaboration between nursing, speech language pathology, and medical staff to streamline processes to encourage screening of all patients admitted for stroke in an effort to reduce occurrence of pneumonitis for high risk stroke patients.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".