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Record W2768792809 · doi:10.1055/s-0037-1607432

Platelets as Modulators of Inflammation

2017· review· en· W2768792809 on OpenAlexaff
Seok‐Joo Kim, Rachelle P. Davis, Craig N. Jenne

Bibliographic record

VenueSeminars in Thrombosis and Hemostasis · 2017
Typereview
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsInflammationHemostasisPlateletImmunologyImmune systemInnate immune systemImmunityPathogenesisAcquired immune systemBiologyMedicine

Abstract

fetched live from OpenAlex

Platelets have classically been considered crucial effector cells in hemostasis, but now are increasingly recognized as players during inflammatory responses in innate and adaptive immunity. Platelets can recognize and kill invading pathogens, and, upon stimulation, also release a wide array of mediators that modify immune and endothelial cell responses. Increased platelet activity can protect the host against infectious insults; however, the excessive activity can lead to inflammation-mediated tissue damage. These critical roles highlight the necessity of balancing the platelet response at the intersection of hemostasis and inflammation. In this review, the authors present the current understanding of the inflammatory role of platelets. They also highlight recent findings on a modulator that links inflammation and deleterious tissue damage in disease pathogenesis.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.109
GPT teacher head0.411
Teacher spread0.302 · 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 teacher head, not a consensus.

Study designOther design
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

Citations50
Published2017
Admission routes1
Has abstractyes

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