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Record W2982558078 · doi:10.1055/s-0039-1697591

Gastrointestinal and Hepatobiliary Disease in Cystic Fibrosis

2019· review· en· W2982558078 on OpenAlexaff
Megan E. Gabel, Gary Galante, Steven D. Freedman

Bibliographic record

VenueSeminars in Respiratory and Critical Care Medicine · 2019
Typereview
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsMedicineCystic fibrosisDiseaseLife expectancyCystic fibrosis transmembrane conductance regulatorIntensive care medicineQuality of life (healthcare)Internal medicineGastroenterology

Abstract

fetched live from OpenAlex

Cystic fibrosis (CF) is a multiorgan disease, and gastrointestinal (GI) manifestations can contribute to significant morbidity and mortality for individuals with CF. Up to 85% of patients with CF experience GI symptoms, thus addressing the GI aspects of this disease is paramount. With the advent of highly effective CF transmembrane conductance regulator modulators that are increasingly available, many individuals with CF now have significantly improved life expectancy. With these advances, GI manifestations that can be a detriment to quality of life such as gastroesophageal reflux disease, dysbiosis, and chronic abdominal pain have become a priority for patients and caregivers. In addition, as individuals have increased longevity, it has become essential for care providers to be aware of topics such as hepatobiliary disease and colorectal cancer screening. An understanding of the wide scope of GI manifestations in CF can enable providers to optimize the overall health and well-being of their patients. In this review, we aim to provide an up-to-date overview of key aspects of GI and hepatic disease in CF.

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 score0.006
Threshold uncertainty score0.021

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 integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.047
GPT teacher head0.396
Teacher spread0.349 · 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

Citations21
Published2019
Admission routes1
Has abstractyes

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