Abstract 16493: Comparison of Left Ventricular Volumes and Ejection Fraction Measured by Non-contrast 2 Dimensional, Contrast 2 Dimensional and 3 Dimensional Transthoracic Echocardiography in Patients With Heart Failure During One Examination, to Measurements by Cardiovascular Magnetic Resonance Imaging
Bibliographic record
Abstract
Introduction: Non-contrast 2D TTE (2D NC TTE) is done in patients with HF to assess LV EDV and ESV and EF. 2D contrast and 3D TTE (2D C TTE, 3D TTE) are also done. Hypothesis: There are no distinct cut-offs for LV volumes for 2D C TTE or 3D TTE volumes. And, it is not known which of these measurements should be reported. The relative differences in these measurements, and their relative accuracy, compared to CMR is unknown. Methods: This is an observational, retrospective analysis of 239 HF patients who had 2D NC, 2D C, 3D TTE, and CMR. We measured bi-plane LV EDV and ESV, ml), and EF (%) in the 2D NC and C TTEs, and 3D LV EDV and ESV were measured in one TTE. Results: Data from 35 of the 239 patients are shown. 2D NC LV volumes were significantly smaller than 2D C volumes (EDV: 189±80 Vs. 227±86 and ESV: 133±68 Vs. 158±77, p<0.001 for both). 2D NC TTE volumes were significantly smaller than 3D TTE volumes (EDV: 189±80 Vs. 206±85 and ESV: 133±68 Vs. 154±70). But, only 2D C EDV was significantly smaller than 3D TTE (227±86 Vs. 206±85, p<0.01), ESV was not significantly different between the two. Compared to CMR, 2D C TTE did not show any significant differences in volumes because contrast was able to identify the compacted myocardial edge. In 3D TTE, only EDV was significantly smaller than CMR (p<0.01) but not 3D TTE ESV, because there is a bigger separation of trabeculated and compacted myocardial edges in ED which is less so in systole. 2D NC TTE EDV and ESV were both significantly smaller (p<0.001) than CMR because the trabeculated myocardial edge is used to measure these. There were no significant differences in EF between the three TTE methods. Figure 1 shows an example. Conclusions: In HF, 2D NC is smaller than 2D C TTE LV volumes, the latter being larger and comparable to CMR. Thus, there is a need to identify specific cut-offs for contrast TTE LV volumes to classify LV size. 3D TTE volumes are comparable to CMR, but smaller than 2D C TTE volumes. Contrast 3D TTE may prove to be most comparable to CMR when this becomes available.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".