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Record W2970559406 · doi:10.1148/ryct.2019180012

Inter- and Intraoperator Variability in Measurement of On-Site CT-derived Fractional Flow Reserve Based on Structural and Fluid Analysis: A Comprehensive Analysis

2019· article· en· W2970559406 on OpenAlex
Kanako K. Kumamaru, Erin Angel, K. Sommer, Vijay Iyer, Michael F. Wilson, Nikhil Agrawal, Aishwarya Bhardwaj, Sharma Kattel, Sandra Kondziela, Saurabh Malhotra, Christopher Manion, Katherine Pogorzelski, Tharmathai Ramanan, Abhishek C. Sawant, Mary M. Suplicki, Sameer Waheed, Shinichiro Fujimoto, Umesh C. Sharma, Frank J. Rybicki, Ciprian N. Ionita

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

VenueRadiology Cardiothoracic Imaging · 2019
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsUniversity of Ottawa
FundersNational Center for Advancing Translational Sciences
KeywordsFractional flow reserveMedicineIntraclass correlationConfidence intervalNuclear medicineStandard deviationCatheterReproducibilityCoronary angiographyRadiologyCardiologyStatisticsInternal medicineMathematics

Abstract

fetched live from OpenAlex

Purpose To measure the inter- and intraobserver variability among operators of varying expertise in conducting CT-derived fractional flow reserve (CT FFR) measurements on-site by using structural and fluid analysis and to evaluate differences in reproducibility between two different training methods for end users. Materials and Methods This retrospective analysis of the prospectively enrolled cohort included 22 symptomatic patients who underwent both 320–detector row coronary CT angiography and catheter-derived fractional flow reserve (FFR) within 90 days. Thirteen operators of varying expertise were assigned to one of two training arms: arm 1, on-site training by a specialist in CT FFR technology; arm 2, self-training through use of written materials. After the training, all 13 operators reviewed the CT data and measured CT FFR in 24 vessels in 22 patients. Inter- and intraoperator variability and agreements between CT FFR and catheter-derived FFR measurements were evaluated. Results The overall intraclass correlation coefficient (ICC) among operators was 0.71 (95% confidence interval: 0.58, 0.83) with a mean absolute difference (± standard deviation) of 0.027 ± 0.022. The operators in arm 2 showed greater interoperator differences than those in arm 1 (0.031 ± 0.024 vs 0.023 ± 0.018; P = .024). Among operators who recalculated CT FFR, the mean CT FFR value did not significantly differ between the first and second calculations (ICC, 0.66; 95% confidence interval: 0.46, 0.87), with the medical specialists producing the lowest intraoperator variability (0.053 ± 0.060). The overall correlation coefficient between CT FFR and catheter FFR was r = 0.61, with a mean absolute difference of 0.096 ± 0.089. Conclusion Good reproducibility of CT FFR values calculated on-site on the basis of structural and fluid analysis was observed among operators of varying expertise. Face-to-face training sessions may cause less variability. Keywords: Adults, CT-Angiography, Cardiac, Computer Applications-General (Informatics), Coronary Arteries © RSNA, 2019 Supplemental material is available for this article.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.710

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 studies0.0000.000
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 head0.014
GPT teacher head0.290
Teacher spread0.276 · 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