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Noninvasive Fractional Flow Reserve Derived From Computed Tomography Angiography for Coronary Lesions of Intermediate Stenosis Severity

2013· article· en· W2548826879 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

VenueCirculation Cardiovascular Imaging · 2013
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineFractional flow reserveStenosisReceiver operating characteristicRadiologyArea under the curvePositive predicative valuePredictive valuePredictive value of testsConfidence intervalComputed tomography angiographyDiagnostic accuracyAngiographyComputed tomographyCardiologyCoronary angiographyInternal medicineMyocardial infarction

Abstract

fetched live from OpenAlex

BACKGROUND: Fractional flow reserve derived from computed tomography angiography (FFRCT) is a noninvasive method for diagnosis of ischemic coronary lesions. To date, the diagnostic performance of FFRCT for lesions of intermediate stenosis severity remains unexamined. METHODS AND RESULTS: Among 407 vessels from 252 patients at 17 centers who underwent CT, FFRCT, invasive coronary angiography, and invasive FFR, we identified 150 vessels of intermediate stenosis by CT, defined as 30% to 69% stenosis. FFRCT, FFR, and CT were interpreted in blinded fashion by independent core laboratories. FFRCT and FFR ≤0.80 were considered hemodynamically significant, whereas CT stenosis ≥50% was considered obstructive. Diagnostic performance of FFRCT versus CT was assessed for accuracy, sensitivity, specificity, positive predictive values, and negative predictive values. Area under the receiver operating characteristic curve and net reclassification improvement were evaluated. For lesions of intermediate stenosis severity, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of FFRCT were 71%, 74%, 67%, 41%, and 90%, whereas accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of CT stenosis were 63%, 34%, 72%, 27%, and 78%. FFRCT demonstrated superior discrimination compared with CT stenosis on per-patient (area under the receiver operating characteristic curve, 0.81 versus 0.50; P=0.0001) and per-vessel basis (area under the receiver operating characteristic curve, 0.79 versus 0.53; P<0.0001). FFRCT demonstrated significant reclassification of CT stenosis for lesion-specific ischemia (net reclassification improvement, 0.45; 95% confidence interval, 0.25-0.65; P=0.01). CONCLUSIONS: FFRCT possesses high diagnostic performance for diagnosis of ischemic for lesions of intermediate stenosis severity. Notably, the high sensitivity and negative predictive value suggest the ability of FFRCT to effectively rule out intermediate lesions that cause ischemia.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.003
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 head0.017
GPT teacher head0.243
Teacher spread0.226 · 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