Selection Criteria in the Era of Perfect Competition for Drug-Eluting Stents in Association With Operator Volumes: An Operator-Volume Analysis of the Selection DES Study
Bibliographic record
Abstract
Background: This study aimed to explore the factors influencing the drug-eluting stent (DES) selection criteria of cardiologists in association with percutaneous coronary intervention (PCI) volumes and to determine whether they value further DES improvements and modifications. Methods: The survey was conducted on a group of cardiologist operators from April 10 to 30, 2023. Results: The analysis included 126 operators who answered the questions. Of these, low-, intermediate-, and high-volume operators accounted for 49 (38.9%), 47 (37.3%), and 30 (23.8%), respectively. Overall, Xience TM everolimus-eluting stent (CoCr-EES) was most frequently used, with > 70% of cardiologists using it in > 20% of their PCI practice. The percentage of selection by low-, intermediate-, and high-volume operators among the DESs used demonstrated no difference, except for dual-therapy sirolimus-eluting and CD34 + antibody-coated Combo ® stent (DTS). Logistic regression analysis revealed that low-volume operators are less likely to be affected in terms of company/sales representative (odds ratio (OR): 0.402, P = 0.031) and bending lesions (OR: 0.339, P = 0.037) for selecting DES. Low-volume operators less frequently selected Resolute Onyx TM zotarolimus-eluting stents (OR: 0.689, P = 0.043) and DTS (Drug-Eluting Stents) (OR: 0.361, P = 0.006) for PCI. Conclusions: The current study results indicate that patient background, DES performance, and product specifications were not criteria for DES selection in cardiologists with different PCI volumes in routine PCI. Cardiol Res. 2024;15(3):189-197 doi: https://doi.org/10.14740/cr1651
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".