Long-Term Follow-Up of Percutaneous Coronary Intervention With Paclitaxel-Eluting Balloon Catheter
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Drug-eluting balloons currently constitute a therapeutic tool used in percutaneous coronary interventions (PCI). Long-term results remain unknown. We evaluated the prognosis of PCI using a second generation paclitaxel-eluting balloon (PEB) in real-world patients. We included all PCI with PEB in de novo or in-stent restenosis coronary lesions performed in our unit from March 2009 to March 2019. We assessed the composite of major adverse cardiovascular events (MACE) rate after a median follow-up of 42 months. Consecutive patients (n = 320) with 386 lesions were included; 46.9% presented with stable angina and 53.1% acute coronary syndromes; 52.6% of the lesions were in-stent restenosis and 47.3% de novo lesions with a mean diameter of 2.4 ± 0.5 mm. A bare metal stent was implanted in 6.7% and a drug-eluting stent in 8.5% of patients. The MACE rate was 8%: 10 (2.6%) cardiovascular deaths, 13 (3.4%) myocardial infarctions, and 16 (4.1%) target lesion revascularization. The all-cause death rate was 5.2%. No cases of thrombosis were recorded. In conclusion, PEB was a safe and effective tool to treat in-stent restenosis and de novo coronary lesions, especially small vessel disease, during long-term follow-up.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it