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Record W2752617160 · doi:10.1161/str.47.suppl_1.tp328

Abstract TP328: Kentucky SEQIP Statewide Approach to Improving Bedside Dysphagia Screening

2016· article· en· W2752617160 on OpenAlexaboutno aff
Kari Moore, Bonita Bobo, Melisa Herbert, Polly Hunt, Peter Rock, Bill Singletary, Debbie Tate, Stephanie Turner, Starr Block

Bibliographic record

VenueStroke · 2016
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDysphagiaStroke (engine)Quality managementCertificationEmergency medicineAuditSwallowingIntensive care medicinePhysical therapySurgery

Abstract

fetched live from OpenAlex

Background and Issues: Research has shown that dysphagia occurs within 3 days of a stroke in 42-67% of patients. Of those, 50% aspirate, leading to higher morbidity and mortality due to complications such as pneumonia, dehydration and malnutrition. After reviewing aggregate Get With The Guidelines data, a voluntary group of Kentucky hospitals named SEQIP (Stroke Encounter Quality Improvement Project) was convened and agreed to share data. SEQIP, which included certified stroke centers and those pursuing certification working in collaboration with the AHA/ASA and the KY Department of Public Health, implemented a statewide QI Plan in an effort to improve the care of stroke patients with regard to bedside dysphagia screening prior to oral intake. Purpose: The purpose of this hospital collaboration was to increase overall compliance of bedside dysphagia screening for acute stroke patients by implementing a unified statewide effort. Methods: Baseline bedside dysphagia screening results were reviewed from 16 SEQIP hospitals. Using an interdisciplinary continuous quality improvement process, SEQIP hospitals shared best practices and dysphagia screening tools, such as Just Add Water, NPO Until You Know, Toronto Bedside Swallowing Screening Test, and other validated screening tools. SEQIP then developed a statewide QI Plan that supported integration of evidence-based bedside dysphagia screening, monitoring, evaluation, reporting and accountability at each member hospital. Results: SEQIP’s participating hospitals achieved improvement in screening rates compared to 2008 baseline data as a direct result of quality improvement techniques. Between 2008 and 2014, SEQIP achieved a 28.9% increase in proportion of eligible patients (n=27616) receiving screening (from 62.87% to 91.81%). SEQIP hospitals demonstrated year-by-year improvement in performance. In the analysis, statistically significant (p<0.001) improvements occurred in every subsequent year compared to baseline. Conclusions: Collaboration between hospitals, sharing of best practices, identification of evidence-based dysphagia screening tools, and the development of a unified QI Plan resulted in improved statewide compliance with bedside dysphagia screening before oral intake.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.640
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.048
GPT teacher head0.368
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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