Factors Influencing 1st and 2nd Generation Drug-Eluting Stent Performance: Understanding the Basic Pharmaceutical Drug-in-Polymer Formulation Factors Contributing to Stent Thrombosis Do We Really Need to Eliminate the Polymer?
Bibliographic record
Abstract
Drug-eluting stents (DES) have a major role in treating cardiovascular disease. The evolution of bare metal stents into 1st generation durable-polymer DES (DP-DES) reduced the rate of in-stent restenosis (ISR) and the need for repeat-revascularization. However, clinical outcomes showed similar rates of late stent thrombosis (ST<1 year) and higher rates of very late stent thrombosis (ST>1 year) necessitating the advent of 2nd generation more biocompatible polymer DES and biodegradable-polymer DES (BP-DES) that reduced ST rates with shorter dual anti-platelet therapy (DAPT). Despite the improvements in drugs and polymer biocompatibility for both durable and biodegradable polymers, stent thrombosis remains an issue. Doubts remain about the safety and efficacy of the more biocompatible 2nd generation durable polymers in respect to vessel inflammatory and thrombogenic response as compared to biodegradable polymers despite clinical trial and meta-analyses evidence indicating that 2nd generation DP-DES are non-inferior to BP-DES for stent thrombosis. A long-term presence of the polymer can cause inflammation and thrombogenesis. However, the cause of stent thrombosis is multi-factorial from a drug-in-polymer formulation perspective; e.g., drug release kinetics, drug physiochemical and pharmacological properties, degradation kinetics; polymer biocompatibility and hemocompatibility and coating properties. It appears that the focus should be on controlling burst release and developing more biocompatible, durable polymers, especially considering the cost of PCI utilizing biodegradable, polymer-free and bioresorbable scaffolds. This may give an insight into certain DP-DES effectiveness as compared to BP-DES for the existing clinical data and improve future stent development.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".