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Record W2685529636 · doi:10.1177/1203475417716364

Adverse Drug Reactions and Cutaneous Manifestations Associated With Anticoagulation

2017· review· en· W2685529636 on OpenAlexaff
Trang Vu, Melinda Gooderham

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2017
Typereview
Languageen
FieldMedicine
TopicHeparin-Induced Thrombocytopenia and Thrombosis
Canadian institutionsProbity Medical ResearchSKiN HealthQueen's UniversityUniversity of Toronto
Fundersnot available
KeywordsMedicineDabigatranEdoxabanRivaroxabanApixabanWarfarinDrugIntensive care medicineDirect thrombin inhibitorAnticoagulantDrug reactionDermatologyAdverse effectPharmacologySurgeryInternal medicineAtrial fibrillation

Abstract

fetched live from OpenAlex

Anticoagulants are amongst the most commonly prescribed medications worldwide. Although rare, localised and systemic drug reactions have been reported with anticoagulants that can lead to significant morbidity and mortality. Some of the first signs of drug reactions to anticoagulants are cutaneous changes that, when recognised early, can prevent significant complications. Dermatologists should be aware of these changes to make an early and accurate diagnosis. This is particularly important in instances of skin-induced necrosis caused by systemic toxicity to anticoagulants. This review discusses adverse drug reactions to the traditional anticoagulants, warfarin and heparin, and the newer direct oral anticoagulants (DOACs) such as the thrombin inhibitor, dabigatran, and the factor Xa inhibitors, rivaroxaban, apixaban, and edoxaban. In particular, this review provides dermatologists with a framework for early diagnosis and management of patients with drug reactions to anticoagulants and alerts them to potential bleeding complications associated with minor procedures.

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)0.002
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.139
GPT teacher head0.374
Teacher spread0.235 · 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

Citations27
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Cutaneous Medicine and SurgerySame topicHeparin-Induced Thrombocytopenia and ThrombosisFrench-language works237,207