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Record W2003589852 · doi:10.1097/mco.0b013e32836599e5

Update on antioxidant micronutrients in the critically ill

2013· review· en· W2003589852 on OpenAlexaff
William Manzanares, Pascal L. Langlois, Gil Hardy

Bibliographic record

VenueCurrent Opinion in Clinical Nutrition & Metabolic Care · 2013
Typereview
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMicronutrientParenteral nutritionMedicineSepsisCritically illSeptic shockIntensive care medicineSeleniumEnteral administrationAntioxidantPhysiologyInternal medicineBiologyChemistryPathology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To evaluate recent evidence on pharmaconutrition with antioxidant micronutrients, for different populations of adult critically ill patients. RECENT FINDINGS: Over the last few years, different studies have shown that high-dose trace elements and vitamins, especially parenteral selenium and zinc, may be able to improve relevant clinical outcomes in the most seriously ill patients. High-dose selenite monotherapy reduces mortality, particularly when a pharmacological loading dose is given in the early stage of severe sepsis and septic shock. Notwithstanding, the recently published REducing Deaths due to OXidative Stress study using an antioxidant cocktail and parenteral selenite, in addition to standard enteral nutrition, was unable to show any benefits for patients with multiple organ failure. SUMMARY: There is evidence supporting the concept of pharmaconutrition with high-dose micronutrients. Selenium therapy may be able to decrease infections and reduce mortality in sepsis, but more research is needed to better understand pharmacokinetics, optimal composition, timing, duration, and dose of antioxidant cocktails for the critically ill.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.202
GPT teacher head0.482
Teacher spread0.280 · 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

Citations36
Published2013
Admission routes1
Has abstractyes

Explore more

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