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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 OpenAlex
Peter J.M. Weijs, Roland N. Dickerson, Daren K. Heyland, Frederick A. Moore, Saúl Rugéles, Stephen A. McClave

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 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.

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)0.002
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.019
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 integrity0.0000.001
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 head0.481
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