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Gender-related differences in post-discharge bleeding among patients with acute coronary syndrome on dual antiplatelet therapy: A BleeMACS sub-study

2018· article· en· W2811018004 on OpenAlexaff
Kajetan Grodecki, Zenon Huczek, Piotr Ścisło, Michał Kowara, Sergio Raposeiras‐Roubín, Fabrizio D’Ascenzo, Emad Abu‐Assi, Josè P.S. Henriques, Jorge Saucedo, José Ramón González‐Juanatey, Stephen B. Wilton, Wouter J. Kikkert, Iván J. Núñez‐Gil, Albert Ariza‐Solé, Xiantao Song, Dimitrios Alexopoulos, Christoph Liebetrau, Tetsuma Kawaji, Claudio Moretti, Shaoping Nie, Toshiharu Fujii, Luís Cláudio Lemos Correia, Masa‐aki Kawashiri, José María García‐Acuña, Danielle A. Southern, Emilio Alfonso, Belén Terol, Alberto Garay, Dongfeng Zhang, Yalei Chen, Ioanna Xanthopoulou, Neriman Osman, Helge Möllmann, Hiroki Shiomi, Francesca Giordana, Silvia Scarano, Fiorenzo Gaita, Xiao Wang, Yan Yan, Jingyao Fan, Yuji Ikari, Takuya Nakahashi, Kenji Sakata, Masakazu Yamagishi, Oliver Kalpak, Saško Kedev, Grzegorz Opolski, Krzysztof J. Filipiak‬

Bibliographic record

VenueThrombosis Research · 2018
Typearticle
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsLibin Cardiovascular Institute of Alberta
Fundersnot available
KeywordsAcute coronary syndromeMedicineInternal medicineCardiologyMyocardial infarction

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.063
GPT teacher head0.331
Teacher spread0.268 · 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 teacher head, not a consensus.

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

Citations29
Published2018
Admission routes1
Has abstractno

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