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Record W1562368019 · doi:10.1159/000341280

Nutrition and Sepsis

2012· review· en· W1562368019 on OpenAlexaff
Jonathan Cohen, Dat N. Chin

Bibliographic record

VenueWorld review of nutrition and dietetics · 2012
Typereview
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsParenteral nutritionMedicineSepsisResuscitationPerioperativeEnteral administrationIntensive care medicineIncidence (geometry)Fish oilSurgeryFish <Actinopterygii>

Abstract

fetched live from OpenAlex

The effect of nutritional support in critically ill patients with sepsis has received much attention in recent years. However, many of the studies have produced conflicting results. As for all critically ill patients, nutritional support, preferably via the enteral route, should be commenced once initial resuscitation and adequate perfusion pressure is achieved. Where enteral feeding is impossible or not tolerated, parenteral nutrition (either as total or complimentary therapy) may safely be administered. Most positive studies relating to nutritional support and sepsis have been in the setting of sepsis prevention. Thus, the administration of standard nutrition formulas to critically ill patients within 24 h of injury or intensive care unit admission may decrease the incidence of pneumonia. Both arginine-supplemented enteral diets, given in the perioperative period, and glutamine-supplemented parenteral nutrition have been shown to decrease infections in surgical patients. Parenteral fish oil lipid emulsions as well as probiotics given in the perioperative period may also reduce infections in patients undergoing major abdominal operations, such as liver transplantation. There is little support at the present time for the positive effect of specific pharmaconutrients, in particular fish oil, probiotics, or antioxidants, in the setting of established sepsis. More studies are clearly required on larger numbers of more homogeneous groups of 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 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)0.000
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 score0.669
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.083
GPT teacher head0.390
Teacher spread0.307 · 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

Citations20
Published2012
Admission routes1
Has abstractyes

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