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Record W2762615536 · doi:10.1097/jom.0000000000001181

Development of a Diabetes Mellitus Knowledge Resource for Clinical Decision Support Assisting Primary Care Physicians With Work-Related Issues

2017· article· en· W2762615536 on OpenAlexaff
Anna K. Allen, Laura S. Welch, Katherine Kirkland, Douglas Trout, Sherry Baron

Bibliographic record

VenueJournal of Occupational and Environmental Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsMerck Canada Inc. (Canada)
FundersNational Institutes of Health
KeywordsMedicineWork (physics)Primary careDiabetes mellitusDecision support systemAffect (linguistics)Health careNursingControl (management)MEDLINEFamily medicinePsychologyComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to describe the process by which a group of subject matter experts (SMEs) in the area of occupational health and primary care developed a clinical decision support (CDS) tool addressing work-related issues, which are important in the care of patients with diabetes mellitus (DM). The CDS is intended for primary care clinicians caring for employed adults with DM. METHODS: The SME's selected guidelines for the management of DM in working adults, reviewed pertinent literature, and developed specific recommendations for action in the clinical setting. RESULTS: Multiple factors at work may adversely affect DM management. Clinicians can support working patients through education and care strategies to improve control. CONCLUSION: Improved recognition of factors at work that can have an impact on DM care provides opportunities for improved management of DM among working adults.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.047
GPT teacher head0.356
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2017
Admission routes1
Has abstractyes

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