MétaCan
Menu
Back to cohort
Record W4292427579 · doi:10.3389/fimmu.2022.918775

Coagulation and complement: Key innate defense participants in a seamless web

2022· review· en· W4292427579 on OpenAlexafffund
Edward L.G. Pryzdial, Alexander Leatherdale, Edward M. Conway

Bibliographic record

VenueFrontiers in Immunology · 2022
Typereview
Languageen
FieldImmunology and Microbiology
TopicComplement system in diseases
Canadian institutionsCanadian Blood ServicesUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaMitacsCanadian Institutes of Health ResearchCanadian Blood ServicesCanada Research ChairsHeart and Stroke Foundation of Canada
KeywordsComplement (music)Complement systemInnate immune systemCoagulationKey (lock)Component (thermodynamics)ImmunologyBiologyCell biologyComputer scienceMedicineComputer securityGenePhenotypeGenetics

Abstract

fetched live from OpenAlex

In 1969, Dr. Oscar Ratnoff, a pioneer in delineating the mechanisms by which coagulation is activated and complement is regulated, wrote, "In the study of biological processes, the accumulation of information is often accelerated by a narrow point of view. The fastest way to investigate the body's defenses against injury is to look individually at such isolated questions as how the blood clots or how complement works. We must constantly remind ourselves that such distinctions are man-made. In life, as in the legal cliché, the devices through which the body protects itself form a seamless web, unwrinkled by our artificialities." Our aim in this review, is to highlight the critical molecular and cellular interactions between coagulation and complement, and how these two major component proteolytic pathways contribute to the seamless web of innate mechanisms that the body uses to protect itself from injury, invading pathogens and foreign surfaces.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.002

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 head0.067
GPT teacher head0.320
Teacher spread0.253 · 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

Citations83
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in ImmunologySame topicComplement system in diseasesFrench-language works237,207