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Record W2098276516 · doi:10.1254/jphs.93.381

Platelets and Anti-platelet Therapy

2003· review· en· W2098276516 on OpenAlexaff
Archibald McNicol, Sara J. Israels

Bibliographic record

VenueJournal of Pharmacological Sciences · 2003
Typereview
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPlateletMedicineThrombusThrombinPharmacologyFibrinPlatelet activationReceptorThromboxane A2ThrombosisThromboxaneInternal medicineImmunology

Abstract

fetched live from OpenAlex

Platelets play a central role in the hemostatic process and consequently are similarly involved in the pathological counterpart, thrombosis. They adhere to various subendothelial proteins, exposed either by injury or disease, and subsequently become activated by the thrombogenic surface or locally produced agonists. These activated platelets aggregate to form a platelet plug, release agonists which recruit more platelets to the growing thrombus, and provide a catalytic surface for thrombin generation and fibrin formation. These platelet-rich thrombi are responsible for the acute occlusion of stenotic vessels and ischemic injury to heart and brain. A range of anti-platelet drugs are currently used, both prophylactically and therapeutically, in regimens to manage thrombo-embolic disorders. These include inhibitors of the generation, or effects, of locally produced agonists; several large clinical trials have supported roles for cyclooxygenase inhibitors, which prevent thromboxane generation, and thienopyridine derivatives, which antagonize ADP receptors. Similarly intravenous alpha IIb beta 3 antagonists have been shown to be effective anti-thrombotics, albeit in highly selective situations; in contrast, to date studies with their oral counterparts have been disappointing. Recent advances in understanding of platelet physiology have suggested several novel, if yet untested, targets for anti-platelet therapy. These include the thrombin receptor, the serotonin handling system, and the leptin receptor.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.125
GPT teacher head0.418
Teacher spread0.292 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations102
Published2003
Admission routes1
Has abstractyes

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