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Coronary Artery Bypass Surgery in Patients with Left Ventricular Dysfunction

2012· article· en· W2322237530 on OpenAlexaff
Eric J. Velazquez, Kerry L. Lee, Marek Deja, Anil Jain, George Sopko, А. В. Марченко, Imtiaz S. Ali, Gerald Pohost, Siniša Gradinac, William T. Abraham, Michael Yii, Dorairaj Prabhakaran, Hanna Szwed, Paolo Ferrazzi, Mark C. Petrie, Christopher M. O’Connor, Pradit Panchavinnin, Lilin She, Robert O. Bonow, Gena Roush Rankin, Robert H. Jones, Jean‐Lucien Rouleau

Bibliographic record

VenueSurvey of Anesthesiology · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsQueen Elizabeth II Health Sciences CentreDalhousie UniversityMontreal Heart Institute
Fundersnot available
KeywordsMedicineCardiologyCoronary artery bypass surgeryInternal medicineArteryBypass surgery

Abstract

fetched live from OpenAlex

Velazquez, Eric J.; Lee, Kerry L.; Deja, Marek A.; Jain, Anil; Sopko, George; Marchenko, Andrey; Ali, Imtiaz S.; Pohost, Gerald; Gradinac, Sinisa; Abraham, William T.; Yii, Michael; Prabhakaran, Dorairaj; Szwed, Hanna; Ferrazzi, Paolo; Petrie, Mark C.; O’Connor, Christopher M.; Panchavinnin, Pradit; She, Lilin; Bonow, Robert O.; Rankin, Gena Roush; Jones, Robert H.; Rouleau, Jean-Lucien for the STICH Investigators Author Information

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)0.000
metaresearch head score (Gemma)0.002
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: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.263
Teacher spread0.246 · 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
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

Citations327
Published2012
Admission routes1
Has abstractyes

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