MétaCan
Menu
Back to cohort
Record W2536640519 · doi:10.1161/atvbaha.116.308198

Monocyte Conversion During Inflammation and Injury

2016· review· en· W2536640519 on OpenAlexafffund
Rachel M. Kratofil, Paul Kubes, Justin Deniset

Bibliographic record

VenueArteriosclerosis Thrombosis and Vascular Biology · 2016
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchAlberta InnovatesHeart and Stroke Foundation of Canada
KeywordsInflammationMonocyteImmunologyInnate immune systemBone marrowAcquired immune systemImmune systemImmunityBiologyMedicine

Abstract

fetched live from OpenAlex

Monocytes are circulating leukocytes important in both innate and adaptive immunity, primarily functioning in immune defense, inflammation, and tissue remodeling. There are 2 subsets of monocytes in mice (3 subsets in humans) that are mobilized from the bone marrow and recruited to sites of inflammation, where they carry out their respective functions in promoting inflammation or facilitating tissue repair. Our understanding of the fate of these monocyte subsets at the site of inflammation is constantly evolving. This brief review highlights the plasticity of monocyte subsets and their conversion during inflammation and injury.

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)0.000
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.035
GPT teacher head0.299
Teacher spread0.264 · 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

Citations520
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueArteriosclerosis Thrombosis and Vascular BiologySame topicImmune cells in cancerFrench-language works237,207