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Record W2134034824 · doi:10.1177/0148607113495570

Appropriate Use of Parenteral Nutrition Through the Perioperative Period

2013· article· en· W2134034824 on OpenAlexaff
Stephen A. McClave, Robert G. Martindale, Beth Taylor, Leah Gramlich

Bibliographic record

VenueJournal of Parenteral and Enteral Nutrition · 2013
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsParenteral nutritionPerioperativeMedicineIntestinal failureIntensive care medicineLipid emulsionEnteral administrationMalnutritionMedical nutrition therapyClinical nutritionShort bowel syndromeSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Recent advances in nutrition therapy of the patient undergoing elective surgery have focused on greater utilization of the gut, feeding closer to the time of surgery, avoiding extensive bowel preparations or use of nasogastric tubes and drains, and measures to promote and maintain intestinal motility. Failure to have protocols in place for delivery of enteral nutrition (EN) through the perioperative period should not lead to inappropriate use of parenteral nutrition (PN) as a default therapy, because in many circumstances, standard therapy with no specialized nutrition support may be associated with better outcome. In cases where EN is not feasible and the patient shows evidence of malnutrition, surgery should be delayed 7-10 days to provide perioperative PN. For patients requiring urgent surgery where EN is not feasible, the initiation of PN postoperatively should be delayed 5-7 days. Whether alternative sources for lipid emulsion and availability of parenteral immune-modulating agents in the future can improve the risk/benefit ratio of PN and expand its use through the perioperative period awaits further study.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.294
Teacher spread0.257 · 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

Citations17
Published2013
Admission routes1
Has abstractyes

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