Bibliographic record
Abstract
Cardiovascular disease (CVD) is a serious threat to human health and life, an important public safety issue, and one of the leading causes of death in the world. Typically, the treatment involves implanting stents in the patient's blood vessels to support the vessels and keep the blood flow open so that oxygen and nutrients can be delivered. This paper will discuss and compare the three main categories of vascular stent materials: 1) organic materials; 2) inorganic materials; and 3) composite materials. Existing bio-organic materials are mostly organic materials that exist in large quantities in the human body and are mostly used as bio-coatings applied to metal bodies, in addition to polyester cardiovascular scaffolds, which are a major category for future development. Inorganic materials are currently the main components of cardiovascular scaffolds, mainly metals, and bio-ceramics. Metals, as the earliest basic materials utilized by mankind, also play a major role in cardiovascular scaffolds. To enhance some specific properties of existing cardiovascular scaffolds, composite materials have been developed, and in the field of materials engineering composite materials are regarded as a major project for future development. This paper will discuss the advantages and disadvantages of each material in turn and explore the future direction of materials in this field. The development of cardiac vascular stent materials will make up for the deficiencies in clinical medicine that cannot be solved by drug-based therapies and is an indispensable part of the development of human science and technology.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".