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Record W6982430547

Implementing dysphagia assessment in stroke patients: Hospital-based education quality improvement project

2015· article· en· W6982430547 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of the Arkansas Academy of Science · 2015
Typearticle
Languageen
FieldMaterials Science
TopicX-ray Diffraction in Crystallography
Canadian institutionsnot available
Fundersnot available
KeywordsDysphagiaStroke (engine)Descriptive statisticsAspiration pneumoniaPneumoniaPrimary care
DOInot available

Abstract

fetched live from OpenAlex

Dysphagia is a prevalent manifestation of stroke. An evidence-based dysphagia assessment is needed to provide quality care to stroke patients. Aims: To provide a descriptive analysis of the patient’s admitted to the hospital with the primary diagnosis of stroke; to evaluate three dysphagia screening tools; and to develop an education program to address training and implementation of the chosen dysphagia assessment tool on pilot units. Design: Descriptive analysis, and an educational program. Setting: Local hospital in Northwest Arkansas. Patients: Pre-data included patients with the primary diagnosis of stroke, over the age of 18, excluding those identified with cognitive impairment, admitted to the local hospital between August 2013 and August 2014. Methodology: Phase I was conducted during May to July 2014 and consisted of comparing three dysphagia screening tools; Barnes Jewish Hospital Stroke Dysphagia Screen, the Toronto, and the Gugging Swallow Screen. Next a retroactive medical record review of patients over the age of 18 admitted to the hospital between August 2013 and August 2014, with the primary diagnosis of stroke was conducted. Patients identified with cognitive impairment were excluded from the study. The charts were evaluated to determine: if a dysphagia assessment was administered and how soon following admission to the hospital, the rate of documented pneumonia (information from nurses notes, physician notes, and chest x-ray), medications, occurrence of diagnostic tests, and bed positioning. Phase II consisted of development and implementation of an education program based on the hospital adopted dysphagia assessment tool. Analysis: A descriptive analysis and summary statistics were performed to summarize the information obtained through a review of medical records from patients admitted with the primary diagnosis of stroke. Results: Of the charts analyzed, 94 met the study’s inclusion criteria. Of the 94 charts analyzed, 23 charts did not include a dysphagia assessment. Of the 94 charts analyzed, 44 charts revealed administration of PO medications before the documentation of a dysphagia assessment. Of the 44 patients who received PO medications before a dysphagia assessment, 12 charts revealed no documentation of one of the diagnostic tests included in the study, in other words, no documentation of a chest x-ray. Of the 12 charts that revealed no chest x-ray, as well as PO medications before a dysphagia assessment, 4 were declared an aspiration risk. Of the 94 charts analyzed, 2 charts had documented pneumonia at discharge. Conclusion: The data in this study shows the need for dysphagia assessment and the literature shows the evidence of this need. While the national organizations have not chosen a superior dysphagia screen, the hospital in this study has. The Barnes Jewish Hospital Stroke Dysphagia Screen/ASDS is the dysphagia screen of choice for the hospital in this study, which was implemented in September 2014. The goal of a bedside dysphagia assessment is to detect those suffering from dysphagia with an easy-to-use tool that can be performed by many professions, including nursing. Therefore, nurses must be aware of this need and solution to care. The organization may implement the assessment, but it is up to the nurses to carry out the implementation. Further assessment should be completed to assess the compliance with the newly implemented dysphagia assessment tool in relation to the education program created and presented to the hospital during this study.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.371
Teacher spread0.340 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2015
Admission routes1
Has abstractyes

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