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Record W2508874241

Use of anticoagulation in elderly patients with atrial fibrillation: What guidelines recommend

2015· article· en· W2508874241 on OpenAlexaboutno aff
Cristóbal Gallego Muñoz, Francisco Javier Ferreira Alfaya, María Eugenia Rodríguez Mateo

Bibliographic record

VenueEuropean journal of clinical pharmacy · 2015
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsAtrial fibrillationMedicineDabigatranAntithromboticClinical PracticeIntensive care medicineRivaroxabanInternal medicineCardiologyWarfarinPhysical therapy
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Atrial fibrillation in the elderly is a complex condition. In recent years, various clinical practice guidelines have been published on patients with atrial fibrillation. The majority of these guidelines make specific recommendations on the clinical characteristics and treatment of elderly patients. In this update, we review the specific comments on the recommendations concerning antithrombotic treatment in elderly patients. Method: An exhaustive search of the published guidelines in referential data sources was performed, up to February 28, 2015. Then, we reviewed the specific comments on the recommendations concerning antithrombotic treatment in elderly patients with non-valvular atrial fibrillation. Results: We included the principal European, American and Canadian clinical practice guidelines. Conclusions: All elderly patients with atrial fibrillation should (unless contraindicated) undergo permanent anticoagulant treatment, although it is important to assess the risk of bleeding. It is important that renal function should be monitored during follow-up, especially when the patient has known renal failure or is taking dabigatran

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)0.006
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.041
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0050.002

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 head0.479
GPT teacher head0.491
Teacher spread0.012 · 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 designSystematic review
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

Citations0
Published2015
Admission routes1
Has abstractyes

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