Drug-Eluting Stents in Preclinical Studies
Bibliographic record
Abstract
Coronary drug-eluting stents are commonplace in clinical practice with acceptable safety and efficacy. Preclinical evaluation of novel drug-eluting stent technologies has great importance for understanding safety and possibly efficacy of these technologies, and well-defined preclinical testing methods clearly benefit multiple communities within the developmental, testing, and clinical evaluation chain. An earlier consensus publication enjoyed widespread adoption but is in need of updating. This publication is an update, presenting an integrated view for testing drug-eluting technologies in preclinical models, including novel devices such as bioabsorbable coatings, totally bioabsorbable stents, bifurcation stents, and stent-free balloon-based drug delivery. This consensus document was produced by preclinical and translational scientists and investigators engaged in interventional technology community. The United States Food and Drug Administration (USFDA) recently issued a Draft Guidance for Industry Document for Drug-Eluting Stents. This expert consensus document is consistent with the Food and Drug Administration guidance. The dynamic nature of this field mandates future modifications and additions that will be added over time.
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 | 0.032 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".