14 Feature tracking versus manual methods of assessment of left atrial mechanics in acute myocardial infarction: a pilot study
Bibliographic record
Abstract
<h3>Background</h3> LA function is conventionally assessed by bi-plane method to compute LA end diastolic volume (LAEDV), end systolic volume (LAESV), stroke volume (LA SV), ejection fraction (LA EF). Voxel feature tracking (FT) is a novel technique for the assessment of LA function. We aimed to investigate if voxel FT derived longitudinal or radial strains are superior to traditionally derived parameters of LA function (LA SV and LA EF) in predicting LV function. <h3>Methods</h3> Twelve-patients underwent CMR at 3T (Achieva CV, Philips Healthcare, Best, The Netherlands) within 3 days following AMI. CMR protocol included: cines and late gadolinium enhancement (LGE) imaging (0.1 mmol/kg gadolinium DTPA). Indexed LAEDV (LAEDVi), end-systolic volume (LAESVi) and ejection fraction were computed by bi-plane method. Voxel FT for the LA in the long axis 4-chamber cines was analysed offline using commercially available software (cvi42 v5.1, Circle Cardiovascular Imaging Inc., Calgary, Canada). <h3>Results</h3> Demographics and basic CMR parameters are mentioned in Table 1. Analysis time was longer for manual contouring and computation of LA EF and SV compared to 4-CH strain analysis (4 ± 2 min versus 2 ± 1.5 min, p = 0.01). On univariate analysis, LV EF was correlated to peak longitudinal strain (PLS) (p = 0.01) and peak radial strain (PRS) (p = 0.02). On multivariate regression analysis, PLS of LA was most strongly correlated to LV EF (R=0.68; p = 0.015). All other parameters did not achieve statistical significance. <h3>Conclusion</h3> Peak left atrial longitudinal strain (PLS) is the only parameter of LA function, which independently correlates to LV ejection fraction. PLS of the LA is easily assessed on 4-chamber cines alone and takes less time to compute than the manually generated parameters of LA function.
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How this classification was reachedexpand
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 | 0.027 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".