VENTRICULAR WALL DYNAMICS
Bibliographic record
Abstract
The Heart Patch ventricular assist device leads to distortion of ventricular chamber shape preventing use of traditional geometry-based volume measurements. Also, tracking of realtime wall dynamics would allow fine-tuning of compression efficacy and possible feedback control of the controller/ driver. We therefore studied ventricular chamber geometry and wall dynamics in response to Heart Patch compression using implanted sonomicrometer sensors. The Heart Patch, sonomicrometer crystals (Sonometrics Corp, Ontario) and flow probes were implanted in three sheep through a limited 4th LICS thoracotomy under general anesthesia. The crystals were placed to record short and long axis information in both ventricles. Studies were performed after seven days when ultrasound signals indicated good acoustic coupling of crystals. The ventricular chambers were divided into two segments each and the distances between crystals determined the shape of each segment. The derived shape determined the optimal algorithm for calculation of ventricular volumes. Typically, 8 crystals provided information on 28 dimensions while filling volumes and pressures were varied in the pneumatically activated Heart Patches. Hemodynamic measurements included ascending aortic blood flow and blood pressure. We conclude that wall dynamics and ventricular volumes in response to an implanted Heart Patch DCC device can be successfully analyzed using a new algorithm based on multiple dimensions obtained from sonomicrometer crystals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".