MétaCan
Menu
Back to cohort
Record W2961557244 · doi:10.1002/ncp.10320

Do We Have Clinical Equipoise (or Uncertainty) About How Much Protein to Provide to Critically Ill Patients?

2019· article· en· W2961557244 on OpenAlexafffund
Jayshil J. Patel, Todd W. Rice, Charlene Compher, Daren K. Heyland

Bibliographic record

VenueNutrition in Clinical Practice · 2019
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsKingston General HospitalClinical Evaluation Research UnitQueen's University
FundersCanadian Institutes of Health ResearchCumberland PharmaceuticalsGlaxoSmithKline
KeywordsMedicineCritically illGuidelineDosingIntensive care medicineClinical trialClinical equipoiseRandomized controlled trialPharmacologyInternal medicinePathology

Abstract

fetched live from OpenAlex

The current recommendation for protein dose in critically ill patients is 1.2-2.0 g/kg/d. Despite this recommendation, there is significant variation in the amount of protein prescribed and delivered worldwide. We contend clinical equipoise, or a state of genuine uncertainty about 2 (dosing) strategies, exists because guideline-based recommendations for protein dose in critically ill patients are rooted in a weak evidentiary base, leaving the clinician with no good basis for choosing a lower or higher protein dose. We outline evidence for and against high protein dose and introduce a pragmatic, registry-based, multicenter, randomized controlled trial, known as EFFORT, which aims to resolve the high vs low protein dose controversy.

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)0.116
metaresearch head score (Gemma)0.372
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.116
Threshold uncertainty score0.612

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1160.372
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.002
Science and technology studies0.0020.012
Scholarly communication0.0080.009
Open science0.0030.005
Research integrity0.0090.012
Insufficient payload (model declined to judge)0.0040.001

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 head0.077
GPT teacher head0.441
Teacher spread0.363 · 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
GenreCommentary

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

Citations13
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueNutrition in Clinical PracticeSame topicClinical Nutrition and GastroenterologyFrench-language works237,207