Angiographic complexity of coronary artery disease according to SYNTAX score and clinical outcomes after revascularisation with newer-generation drug-eluting stents: a substudy of the BIOSCIENCE trial
Bibliographic record
Abstract
AIMS: We sought to assess the performance of drug-eluting stents combining an ultrathin cobalt-chromium platform with a biodegradable polymer across categories of increasing SYNTAX score (SS). METHODS AND RESULTS: Patients included in the BIOSCIENCE trial and randomly allocated to treatment with biodegradable polymer sirolimus-eluting stents (BP-SES) or durable polymer everolimus-eluting stents (DP-EES) were categorised according to SS tertiles (low <8, medium 8-15, high >15). The primary endpoint, target lesion failure (TLF), was defined as a composite of cardiac death, target vessel myocardial infarction and clinically indicated target lesion revascularisation. The patient-oriented endpoint (POCE) included death, myocardial infarction, or any repeat revascularisation. The SS was available in 2,041 out of 2,119 patients (96.3%). At two-year follow-up, patients with an SS >15 experienced higher rates of both TLF and POCE as compared to patients with medium and low SS (14.5% vs. 8.1% and vs. 5.9%, p<0.001; 22.7% vs. 14.9% and vs. 12.4%; p<0.001), respectively. Comparable rates of the composite endpoints were documented for both stent types in each category of SS. CONCLUSIONS: Increasing lesion complexity as assessed by SS was associated with higher rates of TLF and POCE in a contemporary PCI population with minimal exclusion criteria. BP-SES and DP-EES showed comparable performance across the entire spectrum of CAD severity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".