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Record W2894676978 · doi:10.3148/cjdpr-2018-029

Survey of Nutrition Practice in Patients with Severe Sepsis among Canadian Registered Dietitians

2018· article· en· W2894676978 on OpenAlexaffvenueabout
Trisha Baydock, Savita Bector, Lorian Taylor, Gregory Hansen

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2018
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsUniversity of SaskatchewanUniversity of CalgaryHealth Sciences Centre
Fundersnot available
KeywordsMedicineMicronutrientParenteral nutritionSepsisMedical nutrition therapyIntensive care unitFamily medicineClinical nutritionMEDLINEIntensive care medicinePediatricsSurgeryInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to determine the opinions and reported nutrition practices of Canadian Registered Dietitians (RDs) with regard to feeding patients with severe sepsis. METHODS: In 2017, surveys were sent to 112 eligible Canadian RDs in 10 provinces who were practicing in an intensive care environment. The survey included embedded branching logic questions developed to address major facets of sepsis, critical illness, and nutrition. The survey instrument assimilated all data in an anonymous manner, so respondents could not be linked to their answers. RESULTS: Of the 64 RDs who responded (57% response rate), the majority practiced in adult intensive care (81%), within an academic center (59%), and in a mixed unit (73%). A wide variability of Canadian RDs' opinions and practice was reported in determining energy requirements, enteral nutrition (EN) practice, EN with vasoactive agents, parenteral nutrition (PN), and supplemental micronutrients. CONCLUSIONS: Practice variability of Canadian RDs likely reflects gaps in both evidence and guidelines for severe sepsis. Further research efforts are needed to customize nutritional requirements in the patient with evolving sepsis, EN with patients at high risk for gastrointestinal dysfunction, optimizing PN, and the role of micronutrients.

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)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.072
GPT teacher head0.384
Teacher spread0.311 · 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

Citations5
Published2018
Admission routes3
Has abstractyes

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