Diastolic Timed Vibrator: Noninvasive Pre-Hospitalization Treatment of Acute Coronary Ischemia
Bibliographic record
Abstract
The speed of intervention is one of the major factors in increasing the survival rate of patients suffering from acute coronary ischemia. The two principal techniques currently in use: pharmacological and interventional, can be employed to re-canalize coronary arteries, but the former is slow acting and often leads to incomplete reperfusion, while the latter requires specialized personnel in a hospital with a cardiac catheterization laboratory. In this paper, we introduce a novel method intended for pre-hospitalization treatment of patients with acute coronary ischemia that can be safely applied by a minimally trained individual prior to or during patient transportation to hospital. It consists in applying low frequency mechanical vibrations to the left intercostal space of patient's chest during diastole of the heart cycle, to induce vibrations of the heart and thus of the coronary arteries. Mechanical vibrations stimulate mixing of blood which improves drug delivery to the occlusion site, applies mechanical force on the clot leading to its faster dissolution and finally acts as a strong vasodilator in case of spasms. We introduce the principle of operation and the architecture of the Diastolic Timed Vibrator (DTV), including a custom ECG processing algorithm, vibration pattern generator and active braking methods. Experimental results demonstrate the functionality of the DTV device and pave way for in-vivo tests necessary for clinical confirmation of the proposed method.
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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 | 0.000 |
| 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".