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Record W2465964107 · doi:10.1111/nmo.12871

The complex task of measuring intestinal permeability in basic and clinical science

2016· review· en· W2465964107 on OpenAlexafffund
Heather J. Galipeau, Elena F. Verdú

Bibliographic record

VenueNeurogastroenterology & Motility · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDigestive system and related health
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsIntestinal permeabilityTask (project management)Computer scienceMedicineEngineeringInternal medicineSystems engineering

Abstract

fetched live from OpenAlex

Intestinal permeability is a key feature of intestinal barrier function. Altered intestinal permeability is described in many chronic diseases and may be a risk factor for disease development and a target for emerging therapeutics. Thus, reliable and sensitive methods to measure intestinal permeability in both the clinical and preclinical setting are needed. There is currently a large array of tests to choose from, each with advantages and disadvantages. When possible, a combination of methods should be used. The choice of tests should be based on a deep understanding of intestinal barrier physiology and the recognition of their limitations. This mini-review will highlight the advantages and limitations associated with intestinal permeability tests and will identify current problems in the field and how they can be addressed in the future.

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)0.003
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: Review
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.002
Science and technology studies0.0000.002
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.003

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 head0.065
GPT teacher head0.371
Teacher spread0.306 · 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

Citations116
Published2016
Admission routes2
Has abstractyes

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