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Record W2584944958 · doi:10.1093/ehjci/jew328

Three-dimensional echocardiographic quantification of the left-heart chambers using an automated adaptive analytics algorithm: multicentre validation study

2016· article· en· W2584944958 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

VenueEuropean Heart Journal - Cardiovascular Imaging · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsAnalyticsComputer scienceAlgorithmMedicineData mining

Abstract

fetched live from OpenAlex

Aims: Although recommended by current guidelines, adoption of three-dimensional echocardiographic (3DE) chamber quantification in clinical practice has lagged because of time-consuming analysis. We recently validated an automated algorithm that measures left atrial (LA) and left ventricular (LV) volumes and ejection fraction (EF). This study aimed to determine the accuracy and reproducibility of these measurements in a multicentre setting. Methods and results: 180 patients underwent 3DE imaging (Philips) at six sites. Images were analysed using automated HeartModel (HM) software with endocardial border correction when necessary and by manual tracing. Measurements were performed by each site and by the Core Laboratory (CL) as the reference. Inter-technique comparisons included HM measurements by the sites against manual tracing by CL, and showed strong correlations (r-values: LVEDV: 0.97, LVESV: 0.97, LVEF: 0.88, LAV: 0.96), with the automated technique slightly underestimating LV volumes (biases: LVEDV: -14 ± 20 ml, LVESV: -6 ± 20 ml), LVEF (-2 ± 7%) and LAV (-9 ± 10 ml). Intra-technique comparisons included HM measurements by the sites against CL, with and without corrections. Corrections were unnecessary or minimal in most patients, and improved the measurements only modestly. Comparisons without corrections showed perfect agreement for all parameters. With corrections, correlations were better (r-values: LVEDV: 0.99, LVESV: 0.99, LVEF: 0.94, LAV: 0.99) and biases (LVEDV: -8 ± 12 ml, LVESV: -6 ± 12 ml, LVEF: 1 ± 5%, LAV: -10 ± 6 ml) smaller than in inter-technique comparison. All automated measurements with corrections were more reproducible than manual measurements. Conclusion: Automated 3DE analysis of left-heart chambers is an accurate alternative to conventional manual methodology, which yields almost the same values across laboratories and is more reproducible. This technique may contribute towards full integration of 3DE quantification into clinical routine.

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)0.004
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 score0.085
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.049
GPT teacher head0.297
Teacher spread0.248 · 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