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Record W2083040175 · doi:10.1002/pdi.1560

The work of a dedicated inpatient diabetes care team in a district general hospital

2011· article· en· W2083040175 on OpenAlexaboutno aff
AP Brooks, Jsw Li Voon Chong, S Grainger‐Allen, Margaret McDonald, S Nero, Marc Atkin, Sandip deshmukh

Bibliographic record

VenuePractical Diabetes International · 2011
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAuditDiabetes mellitusInpatient careMedical prescriptionEmergency medicineMultidisciplinary teamDiabetes managementFamily medicineQuarter (Canadian coin)Type 2 diabetesMedical emergencyHealth careNursing

Abstract

fetched live from OpenAlex

Abstract We describe the work of a multidisciplinary inpatient diabetes care team in a 400 bed district general hospital over a four‐year period. Also included are some observations on a positive contribution to reduced length of stay for people with diabetes in hospital, and low incidences of prescription and management errors in the first National Diabetes Inpatient Audit in 2009. Specifically between 2005 and 2007 the average length of stay in days for all patients whose diagnosis included diabetes fell from 9.39 to 3.76 days despite the total number of patients increasing from 507 to 633 over the same quarter each year. The inpatient team provided almost 1000 visits to patients with diabetes in the first six months of each year 2008 and 2009, and at the first National Diabetes Inpatient Audit had only 5% prescription errors and 3% management errors (versus 19% and 14% respectively nationally) with 100% appropriate blood glucose testing. We suggest that a dedicated inpatient diabetes care team raises the quality of care for patients and enhances patient and professional education; we also suggest that audit standards should be developed for inpatient diabetes care and assessed in future national audits. Copyright © 2011 John Wiley & Sons.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.026
GPT teacher head0.305
Teacher spread0.279 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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