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Record W2146859491 · doi:10.1177/0884533610373772

Nutrition Treatment of Deficiency and Malnutrition in Chronic Pancreatitis

2010· review· en· W2146859491 on OpenAlexaff
Sinead N. Duggan, Maria OʼSullivan, Sinéad Feehan, Paul F. Ridgway, Kevin C. Conlon

Bibliographic record

VenueNutrition in Clinical Practice · 2010
Typereview
Languageen
FieldMedicine
TopicPancreatitis Pathology and Treatment
Canadian institutionsPancreas Centre (Canada)
Fundersnot available
KeywordsMedicinePancreatitisMalnutritionEndocrine systemExocrine pancreatic insufficiencyNutrition DisordersChronic diseaseInternal medicinePancreatitis, chronicIntensive care medicineDiabetes mellitusGastroenterologyEndocrinologyEnvironmental healthHormone

Abstract

fetched live from OpenAlex

Chronic pancreatitis results in exocrine and endocrine dysfunction, affecting normal digestion and absorption of nutrients. In individuals with chronic pancreatitis, nutrition status may be further affected by poor dietary intake, often related to alcoholism. However, some deficiencies may be overlooked, potentially leading to nutrition-related problems with bone health and fatigue. The aim of this article is to describe the deficiencies that occur and to propose an evidence-based algorithm for the nutrition assessment and treatment of patients with chronic pancreatitis.

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)0.000
metaresearch head score (Gemma)0.001
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: Review
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.113
GPT teacher head0.479
Teacher spread0.366 · 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

Citations63
Published2010
Admission routes1
Has abstractyes

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