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Record W2097909722 · doi:10.1160/th13-11-0918

New oral anticoagulants for stroke prevention in atrial fibrillation: impact of study design, double counting and unexpected findings on interpretation of study results and conclusions

2014· review· en· W2097909722 on OpenAlexaff
Jeremy S. Paikin, Jack Hirsh, Mandy N. Lauw, John W. Eikelboom, Jeffrey S. Ginsberg, Noel Chan

Bibliographic record

VenueThrombosis and Haemostasis · 2014
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsPopulation Health Research Institute
Fundersnot available
KeywordsAtrial fibrillationMedicineStroke (engine)Interpretation (philosophy)CardiologyInternal medicine

Abstract

fetched live from OpenAlex

Four recently introduced new oral anticoagulants (dabigatran, rivaroxaban, apixaban and edoxaban) have been shown to be at least as efficacious and safe as warfarin for stroke prevention in patients with atrial fibrillation in their respective trials. The first three have been approved, while edoxaban is awaiting regulatory approval. Several guidelines have endorsed the approved new oral anticoagulants over warfarin because of their favourable risk-benefit ratio, low propensity for food and drug interactions, and lack of requirement for routine coagulation monitoring. In this invited review, we summarise the results of the four studies and discuss widely held conclusions. We take a step further and discuss how differences in study design, analysis plan, and unexpected events affect the interpretation of the study results. Finally, we take our re-interpretation of study results and discuss how they might impact clinical practice and anticoagulant choice for patients.

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)0.348
metaresearch head score (Gemma)0.477
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.652
Threshold uncertainty score0.804

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3480.477
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.008
Bibliometrics0.0030.004
Science and technology studies0.0010.003
Scholarly communication0.0050.004
Open science0.0050.002
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.275
GPT teacher head0.486
Teacher spread0.211 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designSystematic review
DomainMethods
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

Citations52
Published2014
Admission routes1
Has abstractyes

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