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Record W4417524227 · doi:10.18773/austprescr.2025.052

Injectable drugs for weight management

2025· article· en· W4417524227 on OpenAlexaff
Natasha Yates, Terri-Lynne South

Bibliographic record

VenueAustralian Prescriber · 2025
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsCanadian Fitness and Lifestyle Research Institute
Fundersnot available
KeywordsIncretinWeight lossGallstonesAdverse effectObesityWeight managementGastric emptyingDiabetes mellitusType 2 diabetes

Abstract

fetched live from OpenAlex

Obesity management is complex; medications must be used in conjunction with behavioural changes and monitoring by health professionals. Injectable drugs for weight management include glucagon-like peptide-1 (GLP-1) receptor agonists (e.g. liraglutide, semaglutide) and dual glucose-dependent insulinotropic polypeptide (GIP)/GLP-1 receptor agonists (e.g. tirzepatide). These drugs contribute to weight loss by mimicking the incretin hormones GLP-1 and GIP to reduce appetite, change food enjoyment, slow stomach emptying and stimulate insulin release. Regaining weight is common when these drugs are stopped, so they usually need to be continued long term. Relatively minor gastrointestinal issues are common. There is also a small but real risk of more serious adverse effects, including gallstones and pancreatitis. It is important to monitor mental health, as these drugs can change a patient's relationship with food, and they may be misused by those without obesity.

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)0.004
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: Review · Consensus signal: none
Teacher disagreement score0.097
Threshold uncertainty score0.324

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0970.038

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 head0.015
GPT teacher head0.282
Teacher spread0.267 · 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
GenreReview

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
Published2025
Admission routes1
Has abstractyes

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