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Record W2016142804 · doi:10.4021/jem.v1i2.29

Trace Elements in Critical Illness

2011· article· en· W2016142804 on OpenAlexvenueno aff
Anil Agarwal, Puneet Khanna, Dalim Kumar Baidya, Mahesh Kumar Arora

Bibliographic record

VenueJournal of Endocrinology and Metabolism · 2011
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMicronutrientCritically illIntensive care medicineTRACE (psycholinguistics)Trace MineralsCritical illnessParenteral nutritionIntervention (counseling)Environmental healthNursingPathologyFood scienceBiology

Abstract

fetched live from OpenAlex

Nutritional support of the critically ill patient includes daily provision of macronutrients and micronutrients including vitamins and trace minerals. Publications on nutrition support often emphasize macronutrient administration including proteins, fats, and carbohydrates. Although micronutrient supplementation is common practice, guidelines for their provision during critical illness are completely empiric. Trace elements are essential not only as intermediaries in metabolism but also for their potential roles in wound healing, cellular immunity, and antioxidant activity. This review is aimed at highlighting the important role of trace elements in the metabolic support of patients and also as pharmaconutrients in intensive care units. Future research in ICU nutrition should emphasize on the pharmaconutrient aspect of trace elements so as to determine the appropriate route, dose and timing of the intervention.  doi:10.4021/jem24e

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.054
GPT teacher head0.355
Teacher spread0.301 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations24
Published2011
Admission routes1
Has abstractyes

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