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Record W4309065791 · doi:10.2337/dsi22-0012

Management of Hyperglycemia in the Noncritical Care Setting: A Real-World Case-Based Approach With Alternative Insulin- and Noninsulin-Based Strategies

2022· article· en· W4309065791 on OpenAlexaboutno aff
Samaneh Dowlatshahi, Bhargavi Patham, Jawairia Shakil, Maleeha Zahid, Priya Arunchalam, Abhishek Kansara, Archana Sadhu

Bibliographic record

VenueDiabetes Spectrum · 2022
Typearticle
Languageen
FieldMedicine
TopicHyperglycemia and glycemic control in critically ill and hospitalized patients
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGlycemicHypoglycemiaMetforminInsulinIntensive care medicineRegimenIncretinDiabetes mellitusType 2 diabetesInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Insulin remains the mainstay of treatment for inpatient hyperglycemia in the United States and Canada. However, some other countries commonly use noninsulin agents such as metformin and sulfonylureas, and several trials have demonstrated the efficacy and safety of incretin-based agents in patients with type 2 diabetes who are admitted to noncritical care medicine and surgery services. There is a high degree of interest in alternative glucose-lowering strategies to achieve favorable glycemic outcomes with lower risks of hypoglycemia. In this case series, we highlight the challenges of inpatient glycemic management and the need for alternatives to the traditional basal-bolus insulin regimen. Additional investigation will be imperative to validate the safety and efficacy of appropriate insulin and noninsulin treatments and to further develop guidelines that are applicable in real-world hospital settings.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.257
Teacher spread0.247 · 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 designCase report
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

Citations1
Published2022
Admission routes1
Has abstractyes

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