MétaCan
Menu
Back to cohort
Record W4313402229 · doi:10.1177/02601060221149088

The role of low-carbohydrate diets in the intensive care unit

2023· article· en· W4313402229 on OpenAlex
Julia Marie Hajjar, Claudia Dziegielewski, Sarah Dickson, Allison Simpson, Kwadwo Kyeremanteng

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNutrition and Health · 2023
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsInstitut du Savoir MontfortOttawa Hospital
Fundersnot available
KeywordsMedicineGlycemicMedical nutrition therapyIntensive care unitIntensive care medicineCritically illCarbohydrate metabolismCritical illnessCaloric theoryInsulinInternal medicine

Abstract

fetched live from OpenAlex

Low-carbohydrate, high-fat (LCHF) nutrition therapy is characterized by carbohydrates comprising <26% of the daily caloric intake and a higher proportion of fat. LCHF therapies reduce exogenous glucose load, improve glycemic control, decrease inflammation, and improve clinical outcomes such as respiratory function. Given the altered metabolism in critically ill patients, LCHF nutrition therapy may be especially beneficial as it enables the conservation of protein and glucose for metabolic roles beyond energy use. In critical illness, LCHF diets have the potential to reduce hyperglycemia, improve ventilation, decrease hospital length of stay and reduce hospital costs. The purpose of this commentary piece is to describe LCHF nutrition therapy, summarize its impact on health outcomes, and discuss its role in the intensive care unit (ICU). Additional research on the effects of LCHF nutrition therapy on critically ill patients is warranted, including a focus on COVID-19.

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.

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 score0.292
Threshold uncertainty score0.156

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 head0.057
GPT teacher head0.375
Teacher spread0.318 · 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