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Record W4410742862 · doi:10.1038/s41514-025-00224-1

Metformin administration improves adverse outcomes in older adult burn patients: a single-centre cohort study

2025· article· en· W4410742862 on OpenAlexafffund
Dalia Barayan, Fadi Khalaf, Sarah Rehou, Diana Julia Tedesco, Punit Bhattachan, Gregory R. Pond, Abdikarim Abdullahi, Marc G. Jeschke

Bibliographic record

Venuenpj Aging · 2025
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsPopulation Health Research InstituteSunnybrook Health Science CentreUniversity of TorontoHamilton Health SciencesMcMaster UniversityHealth Sciences CentreSunnybrook Hospital
FundersNational Institute of General Medical SciencesCanadian Institutes of Health ResearchNational Institute on AgingNational Institutes of Health
KeywordsMedicineMetforminCohortAdverse effectCohort studyAdministration (probate law)Emergency medicineInternal medicineInsulin

Abstract

fetched live from OpenAlex

This study assesses the safety and efficacy of metformin administration in older adult burn patients, a rapidly growing demographic with substantially poorer outcomes. This is a single-centre cohort study of older adults (≥60 years) admitted to a provincial burn center over 15 years. Clinical outcomes, laboratory measures, inflammatory markers, and adipose tissue single-nuclei RNA sequencing (SnRNA-seq) were compared among metformin-treated and non-treated controls. A total of 50 metformin-treated and 262 control older burn patients met the eligibility criteria. Despite pre-admission comorbidities, metformin-treated patients showed improved survival, no significant differences in the number of hypoglycemic episodes, a lower incidence of lactic acidosis, and reduced circulating levels of organ damage markers. SnRNA-Seq further revealed that metformin may exert its beneficial effects by local restoration of immune and inflammatory responses. In older burn patients, metformin was linked with improved outcomes and no adverse effects, underscoring its safety and efficacy in this population.

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)0.000
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 score0.009
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.010
GPT teacher head0.280
Teacher spread0.271 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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