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Record W2395068464 · doi:10.1097/mcg.0000000000000334

Gastrointestinal Manifestations, Malnutrition, and Role of Enteral and Parenteral Nutrition in Patients With Scleroderma

2015· review· en· W2395068464 on OpenAlexaff
Shishira Bharadwaj, Parul Tandon, Tushar Gohel, Mandy L. Corrigan, Kathleen L. Coughlin, Abdullah Shatnawei, Soumya Chatterjee, Donald F. Kirby

Bibliographic record

VenueJournal of Clinical Gastroenterology · 2015
Typereview
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineParenteral nutritionMalnutritionScleroderma (fungus)Enteral administrationIntensive care medicineDiseaseInternal medicinePathophysiologyGastrointestinal diseaseGastroenterologyImmunology

Abstract

fetched live from OpenAlex

Scleroderma (systemic sclerosis) is an autoimmune disease that can affect multiple organ systems. Gastrointestinal (GI) involvement is the most common organ system involved in scleroderma. Complications of GI involvement including gastroesophageal reflux disease, small intestinal bacterial overgrowth, and chronic intestinal pseudoobstruction secondary to extensive fibrosis may lead to nutritional deficiencies in these patients. Here, we discuss pathophysiology, progression of GI manifestations, and malnutrition secondary to scleroderma, and the use of enteral and parenteral nutrition to reverse severe nutritional deficiencies. Increased mortality in patients with concurrent malnutrition in systemic sclerosis, as well as the refractory nature of this malnutrition to pharmacologic therapies compels clinicians to provide novel and more invasive interventions in reversing these nutritional deficiencies. Enteral and parenteral nutrition have important implications for patients who are severely malnourished or have compromised GI function as they are relatively safe and have substantial retrospective evidence of success. Increased awareness of these therapeutic options is important when treating scleroderma-associated malnutrition.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.383
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.049
GPT teacher head0.366
Teacher spread0.316 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations65
Published2015
Admission routes1
Has abstractyes

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