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Choosing wisely: The impact of patient selection on efficacy and safety outcomes in the EINSTEIN-DVT/PE and AMPLIFY trials

2016· article· en· W2556140266 on OpenAlexaff
Jan Beyer‐Westendorf, Anthonie W.A. Lensing, Roopen Arya, Henri Bounameaux, Alexander T. Cohen, Philip S. Wells, Saskia Middeldorp, Peter Verhamme, Rodney Hughes, Nils Kucher, Ákos F. Pap, Mila Trajanovic, Martin H. Prins, Paolo Prandoni, Jeffrey I. Weitz

Bibliographic record

VenueThrombosis Research · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsMcMaster UniversityUniversity of OttawaThrombosis and Atherosclerosis Research InstituteOttawa Hospital
FundersAstellas PharmaLEO PharmaBayer HealthCareIonis PharmaceuticalsSanofiGlaxoSmithKlineAstraZenecaDaiichi-SankyoPfizerDaiichi Sankyo EuropeBristol-Myers Squibb
KeywordsMedicineRivaroxabanCohortInternal medicineConfidence intervalRelative riskCohort studyApixabanVenous thromboembolismSurgeryThrombosisWarfarinAtrial fibrillation

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.172
metaresearch head score (Gemma)0.242
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.909

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1720.242
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.010
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0040.005
Open science0.0020.002
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.809
GPT teacher head0.671
Teacher spread0.138 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations19
Published2016
Admission routes1
Has abstractyes

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