Rapid pacing to facilitate transcatheter prosthetic heart valve implantation
Bibliographic record
Abstract
OBJECTIVES: We describe the technique of, and our experience with, rapid ventricular burst pacing to facilitate transcatheter heart valve implantation. BACKGROUND: Endovascular therapeutic procedures frequently require the precise placement of implantable devices. The precision of transcatheter device deployment may be hampered by cardiac motion or the effects of intravascular flow. Burst pacing is associated with a reduction in stroke volume, cardiac output, transvalvular flow, and cardiac motion. METHODS: Rapid pacing was used in 40 consecutive patients with severe aortic stenosis undergoing implantation of catheter-delivered prosthetic valves. Clinical, procedural, and hemodynamic records were reviewed. RESULTS: A mean of 5 +/- 2 burst pacing sequences at rates of 150-220 min(-) (1) were used during balloon valvuloplasty and valve deployment. The duration of pacing required during valve deployment was 12 +/- 3 sec. Pacing was relatively well tolerated when cautiously used with judicious recovery intervals and pressor support. Rapid pacing was associated with a rapid and effective reduction in systemic blood pressure, pulse pressure, transvalvular flow as well as cardiac and catheter motion. CONCLUSIONS: Rapid pacing is a relatively reliable technique to facilitate precise transcatheter deployment of prosthetic heart valves and other endovascular therapeutic devices.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".