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Record W4412190351 · doi:10.4081/ecj.2025.13924

Healthcare professional support: diabetic ketoacidosis avoidance and care

2025· article· en· W4412190351 on OpenAlexaff
Steven James, Stacey Watts, April Hatt, Thuy Frakking, Marc Broadbent, Karen Furlong, Lin Perry, Julia Lowe, Sean Clark

Bibliographic record

VenueEmergency Care Journal · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineDiabetic ketoacidosisHealth careIntensive care medicineNursingDiabetes mellitusEndocrinology

Abstract

fetched live from OpenAlex

Diabetic ketoacidosis is an acute, life-threatening diabetes-related emergency. Our study aimed to explore the support that key healthcare professionals desired around diabetic ketoacidosis avoidance and care within a low socio economic region of Australia. Participants were recruited from a community hospital using the following methods: direct contact of key hospital staff and use of snowballing, posters placed in clinical and non-clinical areas. Audio-taped interview data were analysed using Gibbs’s thematic framework, which entails transcription and familiarisation, code building, theme development, and data consolidation and interpretation. Interviews were conducted with 15 healthcare professionals from across allied health and medical professions. Describing factors relating to diabetic ketoacidosis presentations in people with type 1 diabetes, two themes emerged: a disparity in knowledge and health system opportunities. Findings complement the wealth of literature which details the problem of gaps in support for patient self-care to avoid diabetic ketoacidosis and prevent late presentation of this potentially fatal condition. There is a pressing need to ensure that healthcare professionals have the appropriate level of knowledge to prevent, recognise and treat diabetic ketoacidosis. Service reconfiguration can support care delivery.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.612

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 communication0.0000.000
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 head0.004
GPT teacher head0.266
Teacher spread0.262 · 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
Published2025
Admission routes1
Has abstractyes

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