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Record W2913976280 · doi:10.1111/dom.13661

Intranasal glucagon acutely increases energy expenditure without inducing hyperglycaemia in overweight/obese adults

2019· article· en· W2913976280 on OpenAlexafffund
Priska Stahel, So Jeong Lee, Shawn K. Sud, Alejandro A. Floh, Satya Dash

Bibliographic record

VenueDiabetes Obesity and Metabolism · 2019
Typearticle
Languageen
FieldNeuroscience
TopicRegulation of Appetite and Obesity
Canadian institutionsSickKids FoundationHospital for Sick ChildrenDiabetes CanadaUniversity of Toronto
FundersBanting and Best Diabetes Centre, University of Toronto
KeywordsOverweightMedicineResting energy expenditureCrossover studyAppetiteInternal medicineEndocrinologyGlucagonPlaceboNasal administrationInsulinAdverse effectEnergy expenditureObesityPharmacology

Abstract

fetched live from OpenAlex

AIM: To assess the acute effects of 0.7 mg intranasal glucagon (ING) vs intranasal placebo (INP) on food intake and resting energy expenditure (REE). METHODS: A single-blind, crossover study was conducted in 19 overweight/obese adults (15 men, 4 women). REE was assessed by indirect calorimetry over 90 minutes, after which appetite was assessed using a visual analogue scale, and ad libitum caloric intake was assessed. Plasma samples were obtained at baseline and at 15-minute intervals post-treatment up to 90 minutes. RESULTS: ING increased total REE (INP 61.5 ± 1.2 kcal vs ING 69.4 ± 1.2 kcal; P = 0.027). There were no between-treatment differences in blood glucose, food intake and appetite. There were no adverse effects. CONCLUSION: ING acutely increases REE without increasing plasma glucose. Longer term studies with multiple daily dosing will establish whether this affects body weight.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.211
Teacher spread0.203 · 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 designNon-randomized trial
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

Citations18
Published2019
Admission routes2
Has abstractyes

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