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Record W2944619570 · doi:10.1002/oby.22462

Real‐World Clinical Effectiveness of Liraglutide 3.0 mg for Weight Management in Canada

2019· article· en· W2944619570 on OpenAlexafffundabout
Sean Wharton, Aiden Liu, Arash Pakseresht, Emil Nørtoft, Christiane L. Haase, Johanna Mancini, G. Sarah Power, Sarah VanderLelie, Rebecca Christensen

Bibliographic record

VenueObesity · 2019
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsMerck Canada Inc. (Canada)
FundersCanadian Institutes of Health ResearchMitacsNovo NordiskAstraZenecaObesity CanadaEli Lilly and Company
KeywordsLiraglutideMedicineWeight lossWeight changeCohortPersistence (discontinuity)Body weightWeight managementInternal medicineWeight gainCohort studyObesityGastroenterologyAnimal scienceEndocrinologyType 2 diabetesDiabetes mellitus

Abstract

fetched live from OpenAlex

OBJECTIVE: Real-world clinical effectiveness of liraglutide 3.0 mg, in combination with diet and exercise, was investigated 4 and 6 months post initiation. Changes in absolute and percent body weight were examined from baseline. METHODS: A cohort of liraglutide 3.0 mg initiators in 2015 and 2016 was identified from six Canadian weight-management clinics. Post initiation values at 4 and 6 months were compared with baseline values using a paired t test. RESULTS: , and weight was 114.8 kg. There was a significant change in body weight 6 and 4 months after initiation of treatment in persistent subjects (≥ 6-month: -8.0 kg, P < 0.001; ≥ 4-month: -7.0 kg, P < 0.001) and All Subjects, regardless of persistence (-7.3 kg; P < 0.001). Percentage change in body weight from baseline was -7.1% in the ≥ 6-month group and -6.3% in the ≥ 4-month group, and All Subjects lost 6.5% body weight. Of participants in the ≥ 6-month group, 64.10% and 34.5% lost ≥ 5% and > 10% body weight, respectively. CONCLUSIONS: In a real-world setting, liraglutide 3.0 mg, when combined with diet and exercise, was associated with clinically meaningful weight loss.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.013
GPT teacher head0.286
Teacher spread0.273 · 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.

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

Citations76
Published2019
Admission routes3
Has abstractyes

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