MétaCan
Menu
Back to cohort
Record W4411867403 · doi:10.1016/j.clnesp.2025.06.046

Obesity is associated with lower 30-day mortality in critically ill patients: A retrospective study of over 5400 patients

2025· article· en· W4411867403 on OpenAlexaff
Laurence Genton, Valeria A Bertoni Maluf, François R. Herrmann, Carla M. Prado, Aude de Watteville, Y.M. Dupertuis, Tinh‐Hai Collet, Alexandra Platon, Claudia Paula Heidegger

Bibliographic record

VenueClinical Nutrition ESPEN · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsUniversity of Alberta
FundersHôpitaux Universitaires de Genève
KeywordsMedicineCritically illObesityRetrospective cohort studyObesity paradoxIntensive care medicineEmergency medicineInternal medicineOverweight

Abstract

fetched live from OpenAlex

BACKGROUND: , is associated with higher mortality in the general population but shows a complex relationship with chronic diseases and critically illness. The aim of this study was to determine whether BMI predicted 30-day mortality (primary outcome) and intensive care unit (ICU) and hospital lengths of stay (LOS) (secondary outcome) in critically ill patients. METHODS: ). The association between BMI and outcomes was assessed by multivariate Cox Lasso (Least absolute shrinkage and selection operator) or linear Lasso regression models, adjusted for age, sex, Simplified Acute Physiology Score (SAPS) II, primary diagnosis (ICD-10 codes) and measurement time points. RESULTS: (HR 1.44, p = 0.006) compared to normal weight. BMI categories in the overweight, obesity class I and class II range were all associated with longer ICU and hospital LOS (all p < 0.05). CONCLUSION: Patients with higher BMI, particularly with class II obesity, show a lower 30-day mortality in ICU settings, but a longer ICU and hospital LOS. This is likely a reflection of the obesity paradox and the complex role of body composition in critical illness outcomes. CLINICAL TRIAL REGISTRY: clinicaltrials.gov, identifier: NCT05834894.

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)0.006
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 score0.030
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.339
Teacher spread0.317 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueClinical Nutrition ESPENSame topicCardiovascular Function and Risk FactorsFrench-language works237,207