Estimating epicardial dynamics from the motion of coronary high curvature segments and bifurcation point regions in cineangiography
Bibliographic record
Abstract
Several years ago, Y. Kong et al. (Am. J. Cardiology, vol. 27, p. 529-37, 1971) have shown that the motion of coronary bifurcations closely follows the underlying epicardial motion and therefore could be clinically used as landmarks to study cardiac contraction abnormalities. Here the authors introduce a method that uses single plane cineangiograms to compute this motion, by tracking landmark segments such as bifurcations and high curvature regions. For each pair of successive frames in the sequence, optical flow is used to provide a first estimate of the segment displacement; this is then refined through a local crosscorrelation. The segment position is then updated and the process is repeated for the next image pair in the sequence. This provides sets of translation vectors, i.e. motion fields, which are used to infer the local biaxial contraction patterns. Validation is conducted through a computer model of coronary tree in motion. Results on clinical data are also presented.>
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".