Role of Three-Dimensional Echocardiography in Breast Cancer: Comparison With Two-Dimensional Echocardiography, Multiple-Gated Acquisition Scans, and Cardiac Magnetic Resonance Imaging
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: In patients with breast cancer, the administration of doxorubicin and trastuzumab is associated with an increased risk of cardiotoxicity. Although multiple-gated acquisition (MUGA) scans and two-dimensional transthoracic echocardiography (TTE) are conventional methods for baseline and serial assessment of left ventricular ejection fraction (LVEF) in these patients, little is known about the use of real-time three-dimensional TTE (RT3D TTE) in this clinical setting. The aim of this study was to assess the accuracy of MUGA, 2D TTE, and RT3D TTE for determining LVEF in comparison to cardiac magnetic resonance imaging (CMR). METHODS: Between 2007 and 2009 inclusive, 50 female patients with human epidermal growth factor receptor 2-positive breast cancer received adjuvant trastuzumab after doxorubicin. Serial MUGA, 2D TTE, RT3D TTE, and CMR were performed at baseline, 6, and 12 months after the initiation of trastuzumab. RESULTS: A comparison of left ventricular end diastolic volume (LVEDV) demonstrated a modest correlation between 2D TTE and CMR (r = 0.64 at baseline; r = 0.69 at 12 months, respectively). A comparison of LVEDV between RT3D TTE and CMR demonstrated a stronger correlation (r = 0.87 at baseline; r = 0.95 at 12 months, respectively). Although 2D TTE demonstrated a weak correlation with CMR for LVEF assessment (r = 0.31 at baseline, r = 0.42 at 12 months, respectively), both RT3D TTE and MUGA showed a strong correlation when compared with CMR (r = 0.91 at baseline; r = 0.90 at 12 months, respectively). CONCLUSION: As compared with conventional MUGA, RT3D TTE is a feasible, accurate, and reproducible alternate imaging modality for the serial monitoring of LVEF in patients with breast cancer.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it