TREATMENT OF ATHEROSCLEROSIS THROUGH PENETRATION OF PLAQUE BY SOFT ROBOTS
Bibliographic record
Abstract
A substance called plaque accumulates on the inner walls of arteries due to the consumption of foods high in cholesterol, saturated fats, and so on. The accumulation of plaque deposits leads to a condition known as atherosclerosis, which results in the narrowing and hardening of the arteries. This can lead to coronary artery disease, carotid artery disease, peripheral artery disease, chest pain (also referred to as angina pectoris), heart attack, heart failure, and many more medical complications. The current treatments for atherosclerosis include coronary artery bypass grafting (CABG), angioplasty, percutaneous coronary intervention (PCI), and a number of health and lifestyle changes that could prevent atherosclerotic plaque buildup. These surgeries either involve opening clogged arteries to prevent constriction or directing the blood away from the blocked artery. In both these cases, risks of bleeding, bruising, and complete recovery are prevalent. Therefore, a procedure in which a robot could remove the plaque from the walls of the arteries would greatly increase the success of the treatment and perhaps reduce the risks. This paper intends to review the newly proposed robots for the treatment of atherosclerosis, some of which are magnetically manipulated to drill the plaque to remove it from the arterial wall, and others that are meant to melt the plaque while still preserving the physical integrity of the arteries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".