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Record W3198354146 · doi:10.1016/j.jcct.2021.08.003

Trans-lesional fractional flow reserve gradient as derived from coronary CT improves patient management: ADVANCE registry

2021· article· en· W3198354146 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of cardiovascular computed tomography · 2021
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
FundersDuke Clinical Research InstituteNational Heart, Lung, and Blood InstituteSiemens HealthineersNational Heart Foundation of AustraliaGE HealthcareInternational Communication AssociationNational Health and Medical Research CouncilBoston Scientific CorporationMedtronicSiemensSiemens USAEdwards LifesciencesBiotronikAstraZenecaBayerMerck
KeywordsFractional flow reserveMedicineCardiologyRadiologyInternal medicineCoronary angiographyMyocardial infarction

Abstract

fetched live from OpenAlex

BACKGROUND: ) in guiding downstream testing in patients with stable coronary artery disease (CAD) is unknown. OBJECTIVES: in predicting early revascularization and improving efficiency of catheter laboratory utilization. MATERIALS: across visible stenosis. RESULTS: >0.13, would potentially reduce ICA by 32.2% (1638-1110, p ​< ​0.001) and improve the revascularization to ICA ratio from 65.2% to 73.1%. CONCLUSIONS: has the potential to aid decision-making for ICA referral and improve efficiency of catheter laboratory utilization.

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.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.232
Teacher spread0.223 · 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