MétaCan
Menu
Back to cohort

Immune Responses in the Liver

2018· review· en· W2783469772 on OpenAlexaff
Paul Kubes, Craig N. Jenne

Bibliographic record

VenueAnnual Review of Immunology · 2018
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsImmune systemBiologyImmunityInflammationImmunologyInnate immune systemLiver injuryAcquired immune system

Abstract

fetched live from OpenAlex

The liver is a key, frontline immune tissue. Ideally positioned to detect pathogens entering the body via the gut, the liver appears designed to detect, capture, and clear bacteria, viruses, and macromolecules. Containing the largest collection of phagocytic cells in the body, this organ is an important barrier between us and the outside world. Importantly, as portal blood also transports a large number of foreign but harmless molecules (e.g., food antigens), the liver's default immune status is anti-inflammatory or immunotolerant; however, under appropriate conditions, the liver is able to mount a rapid and robust immune response. This balance between immunity and tolerance is essential to liver function. Excessive inflammation in the absence of infection leads to sterile liver injury, tissue damage, and remodeling; insufficient immunity allows for chronic infection and cancer. Dynamic interactions between the numerous populations of immune cells in the liver are key to maintaining this balance and overall tissue health.

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)0.001
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.003

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 head0.030
GPT teacher head0.325
Teacher spread0.295 · 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

Citations893
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueAnnual Review of ImmunologySame topicImmune Cell Function and InteractionFrench-language works237,207