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Parenteral nutrition in the intensive care unit

2012· review· en· W2138332006 on OpenAlexaff
Khursheed N. Jeejeebhoy

Bibliographic record

VenueNutrition Reviews · 2012
Typereview
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsParenteral nutritionMedicineMalnutritionMicronutrientIntensive care unitSepsisEnteral administrationIntensive care medicineOvernutritionGastrointestinal tractIntensive careInternal medicine

Abstract

fetched live from OpenAlex

Patients in the intensive care unit (ICU) are unable to nourish themselves orally. In addition, critical illness increases nutrient requirements as well as alters metabolism. Typically, ICU patients rapidly become malnourished unless they are provided with involuntary feeding either through a tube inserted into the GI tract, called enteral nutrition (EN), or directly into the bloodstream, called parenteral nutrition (PN). Between the 1960s and the 1980s, PN was the modality of choice and the premise was that if some is good, more is better, which led to overfeeding regimens called hyperalimentation. Later, the dangers of overfeeding, hyperglycemia, fatty liver, and increased sepsis associated with PN became recognized. In contrast, EN was not associated with these risks and it gradually became the modality of choice in the ICU. However, ICU patients in whom the gastrointestinal tract was nonfunctional (i.e., gut failure) required PN to avoid malnutrition. In addition, EN was shown, on average, to not meet nutrient requirements, and underfeeding was recognized to increase complications because of malnutrition. Hence, the balanced perspective has been reached of using EN when possible but avoiding underfeeding by supplementing with PN when required. This new role for PN is currently being debated and studied. In addition, the relative merits and needs for protein, carbohydrates, lipids, and micronutrients are areas of study.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.826
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.214
GPT teacher head0.437
Teacher spread0.223 · 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 teacher head, not a consensus.

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

Citations40
Published2012
Admission routes1
Has abstractyes

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