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Record W2150510356 · doi:10.1017/s002966510000063x

Parenteral nutrition in the critically-ill patient: more harm than good?

2000· review· en· W2150510356 on OpenAlexaff
Daren K. Heyland

Bibliographic record

VenueProceedings of The Nutrition Society · 2000
Typereview
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsQueen's University
Fundersnot available
KeywordsCritically illParenteral nutritionMedicineIntensive care medicineCritical illness

Abstract

fetched live from OpenAlex

While many studies have reported that providing parenteral nutrition (PN) can change nutritional outcomes, there are limited data that demonstrate that PN influences clinically-important end points in critically-ill patients. The purpose of the present paper is to systematically review and critically appraise the literature to examine the relationship between PN and morbidity and mortality in the critically-ill patient. Studies comparing enteral nutrition (EN) with PN and studies comparing PN with no PN were reviewed. The results suggest that EN is associated with reduced infectious complications in some critically-ill subgroups. PN, on the other hand, is associated with increased morbidity and mortality in critically-ill patients. When nutritional support is indicated, EN should be used preferentially over PN. Further studies are needed to define the optimal timing and composition of PN in patients not tolerating sufficient EN. Strategies to optimize EN delivery and minimize PN utilization in critically-ill patients are indicated.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.043
GPT teacher head0.347
Teacher spread0.304 · 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

Citations29
Published2000
Admission routes1
Has abstractyes

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