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Record W2611687650 · doi:10.3390/nu9050466

Parenteral Nutrition and Intestinal Failure

2017· review· en· W2611687650 on OpenAlexaff
Barbara Bielawska, Johane P. Allard

Bibliographic record

VenueNutrients · 2017
Typereview
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsToronto General HospitalUniversity Health NetworkQueen's UniversityUniversity of Toronto
Fundersnot available
KeywordsIntestinal failureParenteral nutritionMedicineIntensive care medicineBiology

Abstract

fetched live from OpenAlex

Severe short bowel syndrome (SBS) is a major cause of chronic (Type 3) intestinal failure (IF) where structural and functional changes contribute to malabsorption and risk of micronutrient deficiencies. Chronic IF may be reversible, depending on anatomy and intestinal adaptation, but most patients require long-term nutritional support, generally in the form of parenteral nutrition (PN). SBS management begins with dietary changes and pharmacologic therapies taking into account individual anatomy and physiology, but these are rarely sufficient to avoid PN. New hormonal therapies targeting intestinal adaptation hold promise. Surgical options for SBS including intestinal transplant are available, but have significant limitations. Home PN (HPN) is therefore the mainstay of treatment for severe SBS. HPN involves chronic administration of macronutrients, micronutrients, fluid, and electrolytes via central venous access in the patient's home. HPN requires careful clinical and biochemical monitoring. Main complications of HPN are related to venous access (infection, thrombosis) and metabolic complications including intestinal failure associated liver disease (IFALD). Although HPN significantly impacts quality of life, outcomes are generally good and survival is mostly determined by the underlying disease. As chronic intestinal failure is a rare disease, registries are a promising strategy for studying HPN patients to improve outcomes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.920
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.125
GPT teacher head0.419
Teacher spread0.294 · 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.

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

Citations106
Published2017
Admission routes1
Has abstractyes

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