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Record W2578630124 · doi:10.1177/0148607116682003

Proceedings of the 2016 Clinical Nutrition Week Research Workshop—The Optimal Dose of Protein Provided to Critically Ill Patients

2017· review· en· W2578630124 on OpenAlexaff
Daren K. Heyland, Olav Rooyakers, Marina Mourtzakis, Renee D. Stapleton

Bibliographic record

VenueJournal of Parenteral and Enteral Nutrition · 2017
Typereview
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsUniversity of WaterlooClinical Evaluation Research UnitKingston General Hospital
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of HealthAbbott Fund
KeywordsCritically illIntensive care medicineMedicineIntensive care unitConfusionIntensive carePsychology

Abstract

fetched live from OpenAlex

Recent literature has created considerable confusion about the optimal amount of protein/amino acids that should be provided to the critically ill patient. In fact, the evidentiary basis that directly tries to answer this question is relatively small. As a clinical nutrition research community, there is an urgent need to develop the optimal methods to assess the impact of exogenous protein/amino acid administration in the intensive care unit setting. That assessment can be conducted at various levels: (1) impact on stress response pathways, (2) impact on muscle synthesis and protein balance, (3) impact on muscle mass and function, and (4) impact on the patient's recovery. The objective of this research workshop was to review current literature relating to protein/amino acid administration for the critically ill patient and clinical outcomes and to discuss the key measurement and methodological features of future studies that should be done to inform the optimal protein/amino acid dose provided to critically ill patients.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.193
GPT teacher head0.466
Teacher spread0.274 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations15
Published2017
Admission routes1
Has abstractyes

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