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Record W2622607558 · doi:10.1186/s13054-017-1690-5

Role of nutrition support in adult cardiac surgery: a consensus statement from an International Multidisciplinary Expert Group on Nutrition in Cardiac Surgery

2017· article· en· W2622607558 on OpenAlexaffabout
Christian Stoppe, Andreas Goetzenich, Glenn Whitman, Rika Ohkuma, Trish Brown, Roupen Hatzakorzian, Arnold S. Kristof, Patrick Meybohm, Jefferey Mechanick, Adam S. Evans, D. Dante Yeh, Bernard McDonald, Philip M. Jones, Richard G. Barton, Ravi Tripathi, Gunnar Elke, Oliver J. Liakopoulos, Ravi Agarwala, В. В. Ломиворотов, Ekaterina Nesterova, Gernot Marx, Carina Benstoem, Margot Lemieux, Daren K. Heyland

Bibliographic record

VenueCritical Care · 2017
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsKingston General HospitalQueen's UniversityWestern UniversityUniversity of OttawaClinical Evaluation Research UnitRoyal Victoria HospitalMcGill University Health CentreRoyal Victoria Regional Health Centre
FundersB. Braun Melsungen
KeywordsMedicineMultidisciplinary approachCardiac surgeryIntensive care medicineCardiopulmonary bypassMedical nutrition therapyMEDLINESurgeryInternal medicine

Abstract

fetched live from OpenAlex

Nutrition support is a necessary therapy for critically ill cardiac surgery patients. However, conclusive evidence for this population, consisting of well-conducted clinical trials is lacking. To clarify optimal strategies to improve outcomes, an international multidisciplinary group of 25 experts from different clinical specialties from Germany, Canada, Greece, USA and Russia discussed potential approaches to identify patients who may benefit from nutrition support, when best to initiate nutrition support, and the potential use of pharmaco-nutrition to modulate the inflammatory response to cardiopulmonary bypass. Despite conspicuous knowledge and evidence gaps, a rational nutritional support therapy is presented to benefit patients undergoing cardiac surgery.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.058
GPT teacher head0.403
Teacher spread0.345 · 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.

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

Citations135
Published2017
Admission routes2
Has abstractyes

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