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Record W2589003411 · doi:10.5430/jha.v6n2p15

Carbohydrate knowledge in diabetic emergency department patients at an academic institution

2017· article· en· W2589003411 on OpenAlexvenueno aff
Preeti Dalawari, David Sprowls, Vicki Moran, Eric S. Armbrecht

Bibliographic record

VenueJournal of Hospital Administration · 2017
Typearticle
Languageen
FieldMedicine
TopicHyperglycemia and glycemic control in critically ill and hospitalized patients
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDiabetes mellitusPopulationEmergency departmentBlood sugarDescriptive statisticsFamily medicineGerontologyNursingEnvironmental healthEndocrinology

Abstract

fetched live from OpenAlex

Background: Diabetes Mellitus (DM) affects 12.3% of the U.S. population and is responsible for $245 billion in annual costs. Knowledge about their disease is an important part of patients’ self-management.Objective: The objective of this study was to describe the baseline level of knowledge of patients with diabetes in this emergency department (ED), including behaviors related to healthy eating such as carbohydrate counting (CC).Methods: This was a cross sectional interview survey conducted at an academic tertiary center. An 8-item survey was developed to assess areas of diabetes self-care and carbohydrate knowledge. Trained research assistants approached all medically stable, non-pregnant ED patients with a past medical history of diabetes for participation. Descriptive statistics and ANOVA analysis were used.Results: Of the 115 patients approached, 98 were willing to participate; 54% were using insulin and 68% were female. The average age was 55 (SD +/- 14) years and diagnosed for an average of 12 (SD +/- 10) years. Fifty three percent did not check their morning blood sugar. Only 20% could accurately state the target hemoglobin A1c. While 48% of participants could relate the importance of carbohydrates to blood sugar, only 5% could state the number of grams of carbohydrates in a slice of bread. Only 1 participant correctly answered all 4 of the carbohydrate questions. Higher education and more visits with a nutritionist were associated with carbohydrate knowledge.Conclusions: Carbohydrate knowledge among this ED population was poor. Opportunities exist for patient education.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.021
GPT teacher head0.330
Teacher spread0.310 · 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
Published2017
Admission routes1
Has abstractyes

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