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Record W3004010697 · doi:10.1002/ncp.10456

Dispelling Myths and Unfounded Practices About Enteral Nutrition

2020· article· en· W3004010697 on OpenAlexaff
Stephanie Zoeller, Matthew L. Bechtold, Berri Burns, Theresa Cattell, Brandee Grenda, Lindsey Haffke, Cara Larimer, Jan Powers, Fred Reuning, Lauren Tweel, Peggi Guenter

Bibliographic record

VenueNutrition in Clinical Practice · 2020
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsChinook Regional HospitalAlberta Health Services
Fundersnot available
KeywordsMedicineEnteral administrationMythologyIntensive care medicineParenteral nutrition

Abstract

fetched live from OpenAlex

Many protocols and steps in the process of enteral nutrition (EN) use are not overly supported with strong research and have been done the same way over many years without questioning the use of best-practices evidence. This article reports many of the myths and unfounded practices surrounding EN and attempts to refute those myths with current evidence. These practices include those about enteral access devices, formulas, enteral administration, and complications.

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)0.129
metaresearch head score (Gemma)0.267
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.129
Threshold uncertainty score0.681

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1290.267
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0090.104
Scholarly communication0.0120.030
Open science0.0040.011
Research integrity0.0120.039
Insufficient payload (model declined to judge)0.0020.001

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 head0.111
GPT teacher head0.449
Teacher spread0.338 · 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
GenreCommentary

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
Published2020
Admission routes1
Has abstractyes

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