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Record W3103179996 · doi:10.1016/j.xjon.2020.10.010

What should be the role of fractional flow reserve measurement in patients undergoing coronary artery bypass grafting?

2020· editorial· en· W3103179996 on OpenAlexaffabout
David Glineur, Aun‐Yeong Chong, Juan B. Grau

Bibliographic record

VenueJTCVS Open · 2020
Typeeditorial
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFractional flow reserveMedicineConventional PCICardiologyRevascularizationBypass surgeryCulpritContext (archaeology)ArteryBypass graftingPsychological interventionPercutaneousInternal medicineSurgeryMyocardial infarctionCoronary angiography

Abstract

fetched live from OpenAlex

Feature Editor's Introduction—Glineur and colleagues from Ottawa present a superb summary on the evidence of the use of fractional flow reserve (FFR) for coronary bypass surgery.FFR was conceived to guide percutaneous coronary interventions, a procedure that targets directly the culprit plaque and whose success is inversely related to the complexity and the numbers of the lesions treated. In this context, a tool able to identify the flow-limiting lesions and reduce the procedural complexity and the number of stents implanted makes obvious sense.

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)0.013
metaresearch head score (Gemma)0.112
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.112
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0040.006
Open science0.0040.001
Research integrity0.0240.031
Insufficient payload (model declined to judge)0.0080.005

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 head0.056
GPT teacher head0.324
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 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
GenreEditorial

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

Citations4
Published2020
Admission routes2
Has abstractyes

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