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Record W3199533122 · doi:10.3390/jcm10184185

Direct Oral Anticoagulants in Patients with Obesity and Atrial Fibrillation: Position Paper of Italian National Association of Hospital Cardiologists (ANMCO)

2021· review· en· W3199533122 on OpenAlexaff
David Mocini, Stefania Angela Di Fusco, Edoardo Mocini, Lorenzo M. Donini, Carlo Lavalle, Andrea Di Lenarda, Carmine Riccio, Pasquale Caldarola, Leonardo De Luca, Michele Massimo Gulizia, Fabrizio Oliva, Domenico Gabrielli, Furio Colivicchi

Bibliographic record

VenueJournal of Clinical Medicine · 2021
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsHealth Care Foundation
Fundersnot available
KeywordsMedicineEdoxabanApixabanAtrial fibrillationDabigatranRivaroxabanWarfarinObservational studyRandomized controlled trialInternal medicine

Abstract

fetched live from OpenAlex

The use of the direct oral anticoagulants dabigatran, rivaroxaban, apixaban and edoxaban (DOACs) offers some major advantages over warfarin and other vitamin K antagonists (VKAs). One advantage is the possibility to use a fixed dose in normal-weight patients, overweight patients and patients with obesity. However, the "one size fits all" strategy raised a concern regarding the possibility to undertreat patients with a high body mass index. No randomized controlled trials (RCTs) have ever compared VKAs and DOACs in this population. We analyzed data from the literature on DOAC pharmacokinetics and pharmacodynamics, results from the four pivotal phase III trials on non-valvular atrial fibrillation, retrospective observational studies and metanalyses. While we are aware of the limitation imposed by the absence of specific RCTs, we propose the position of the Italian Association of Hospital Cardiologists (ANMCO) on the use of DOACs in patients with obesity based on the existing evidence.

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)0.003
metaresearch head score (Gemma)0.007
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: Editorial · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.100
GPT teacher head0.437
Teacher spread0.337 · 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
GenreEditorial

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

Citations15
Published2021
Admission routes1
Has abstractyes

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