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Record W2591420985 · doi:10.1177/0884533617692774

Experimental and Outcome‐Based Approaches to Protein Requirements in the Intensive Care Unit

2017· article· en· W2591420985 on OpenAlexaff
Peter J.M. Weijs, Roland N. Dickerson, Daren K. Heyland, Frederick A. Moore, Saúl Rugéles, Stephen A. McClave

Bibliographic record

VenueNutrition in Clinical Practice · 2017
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsKingston General HospitalClinical Evaluation Research UnitQueen's University
FundersNestlé Nutrition Institute
KeywordsMedicineObservational studyIntensive care unitRandomized controlled trialIntensive care medicineOutcome (game theory)Critical illnessIntensive careCritically illInternal medicine

Abstract

fetched live from OpenAlex

Insight into protein requirements of intensive care unit (ICU) patients is urgently needed, but at present, it is unrealistic to define protein requirements for different diagnostic groups of critical illness or at different stages of illness. No large randomized controlled trials have randomized protein delivery, adequately addressed energy intake, and evaluated relevant clinical outcomes. As a pragmatic approach, experimental studies have focused on protein requirements of heterogeneous ICU patients. Data are scarce and the absolute value of protein requirements therefore is an approximation. Experimental studies indicate a protein requirement of >1.2 g/kg protein, which is supported by several outcome-based observational studies. Protein intake levels of up to 2.0-2.5 g/kg appear to be safe. A higher level of personalized treatment, within 1.2 and 2.5 g/kg, must involve identification of patients with low muscle protein mass that might benefit most from adequate protein nutrition in the ICU.

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)0.039
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.038
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.481
GPT teacher head0.513
Teacher spread0.032 · 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 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

Citations11
Published2017
Admission routes1
Has abstractyes

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