One-year clinical outcomes of patients treated with everolimus-eluting bioresorbable vascular scaffolds versus everolimus-eluting metallic stents: a propensity score comparison of patients enrolled in the ABSORB EXTEND and SPIRIT trials
Bibliographic record
Abstract
AIMS: We sought to compare the outcomes of low/moderate complexity patients treated with the Absorb BVS from the ABSORB EXTEND trial with patients treated with the XIENCE everolimus-eluting stent (EES), using propensity score (PS) matching of pooled data from the SPIRIT trials (SPIRIT II, SPIRIT III, SPIRIT IV) and the XIENCE V USA trial. METHODS AND RESULTS: ABSORB EXTEND was a prospective, single-arm, open-label clinical study in which 812 patients were enrolled at 56 sites. This study allowed the treatment of lesions ≤28 mm in length and with a reference vessel diameter of 2.0-3.8 mm (as assessed by online QCA). The propensity score was obtained by fitting a logistic regression model with the cohort indicator as the binary outcome and other variables as the predictor variables. At one-year clinical follow-up, there was no statistical difference between groups with regard to MACE (5.0% vs. 4.8%, p=0.83), target lesion failure (5.0% vs. 4.7%, p=0.74), ischaemia-driven target vessel revascularisation (2.3% vs. 3.0%, p=0.38) and device thrombosis (1.0% vs. 0.3%, p=0.11). Myocardial infarction was higher with Absorb (3.3% vs. 1.5%, p=0.02), at the expense of periprocedural CK-MB elevation. Independent predictors of MACE among patients receiving Absorb BVS were treatment of multivessel disease, insulin-dependent diabetes and performance of post-dilation. CONCLUSIONS: At one-year follow-up, propensity score-matched analysis demonstrated that the clinical safety and effectiveness of Absorb are comparable to those of XIENCE EES among non-complex patients treated with PCI.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".