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Record W4313464202 · doi:10.22270/ajprd.v10i4.1158

Review: Drug Discovery and Development of Warfarin

2022· article· en· W4313464202 on OpenAlexaboutno aff
Tirsa Ami Manao, Ridho Asra, Boy Chandra

Bibliographic record

VenueAsian Journal of Pharmaceutical Research and Development · 2022
Typearticle
Languageen
FieldComputer Science
TopicEdcuational Technology Systems
Canadian institutionsnot available
Fundersnot available
KeywordsWarfarinRodenticideMedicineFood and drug administrationAnticoagulant drugBlood clottingNew drug applicationClinical trialDrugAnticoagulantDabigatranPharmacologyIntensive care medicineSurgeryInternal medicineToxicologyBiology

Abstract

fetched live from OpenAlex

Background: The history of the discovery of Warfarin started from the plains of North America in Canada in 1920. Livestock in that area died from bleeding. Warfarin was first used in 1948 as a rodenticide, and in 1954 the US Food and Drug Administration (FDA) approved it for medical use as an anticoagulant. Purpose: This review article aims to discuss the history of the discovery of warfarin starting from the presence of blood clotting disorders to the point that researchers worked to find drugs that can inhibit blood clotting, namely the anticoagulant group. Research Methods: The method used is the study of relevant literature which is accessed through online sites such as Google Scholar, Research Gate, Science Direct, Springer Link, and NCBI. Conclusion: In its development, several trials such as in silico, preclinical, and clinical trials have shown significant results but are always associated with bleeding.

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)0.002
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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.004

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 head0.148
GPT teacher head0.425
Teacher spread0.278 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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