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Record W4306725164 · doi:10.1093/occmed/kqac057

Practical Diabetes Care for Healthcare Professionals

2022· article· en· W4306725164 on OpenAlexaboutno aff
Anna Trakoli

Bibliographic record

VenueOccupational Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
Fundersnot available
KeywordsGuidelineAction (physics)Diabetes mellitusHealth careMedicineHealth professionalsTable (database)NursingMedical educationComputer sciencePolitical sciencePathology

Abstract

fetched live from OpenAlex

This is the second edition of a paperback on the increasingly prevalent chronic condition of diabetes. The book author is a Professor of Endocrinology and Metabolism at the University of Manitoba, Canada. The author’s objective is to ‘provide the practical aspect to translate guideline dissemination into daily implementation’, acknowledging that evidence-based clinical guidelines are broad-based and therefore not practical. The target audience is all healthcare professionals involved in diabetes care. The book covers all important aspects of clinical diabetes care. The individual chapters examine the organization of diabetes care in the community and in the hospital, the use of technology in the management of diabetes, the acute and chronic complications of diabetes, and treatment considerations in the elderly and pregnancy. Each chapter starts with an abstract which summarizes key concepts and ends with a reference list of major sources. Real-life case studies are used where appropriate to enhance understanding and consolidate learning. The text is supplemented with informative tables; this includes an easy-reference 17-page table of all currently available oral and injectable medications, outlining their mechanism of action, dose, action time, benefits and disadvantages. The book can be read from cover to cover, but it is also easy to dip into to locate information of interest.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.999

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)0.0020.000

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 head0.063
GPT teacher head0.433
Teacher spread0.370 · 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
Published2022
Admission routes1
Has abstractyes

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