Treatment of Calcified Lesions Using a Dedicated Super-High Pressure Balloon: Multicenter Optical Coherence Tomography Registry
Bibliographic record
Abstract
BACKGROUND: Calcified lesions often lead to difficulty achieving optimal stent expansion. OPN non-compliant (NC) is a twin layer balloon with high rated burst pressure that may modify calcium effectively. METHODS: arc were included. OCT was performed in all cases before and after OPN NC, and after intervention. Primary efficacy endpoints were frequency of expansion (EXP) ≥80 % of the mean reference lumen area and mean final EXP by OCT, and secondary endpoints were calcium fractures (CF), and EXP ≥90 %. RESULTS: 50 cases were included; 25 (50 %) superficial, and 25 (50 %) nodular. Calcium score of 4 in 42 (84 %) cases and 3 in 8 (16 %). OPN NC was used alone, or after other devices if further modification was needed, NC in 27 (54 %), cutting in 29 (58 %), scoring in 1 (2 %), IVL in 2 (4 %); or if non-crossable lesion, rotablation in 5 (10 %) cases. EXP ≥80 % was achieved in 40 (80 %) cases with mean final EXP post intervention of 85.7 % ± 8.9. CF were documented in 49 (98 %) cases; multiple in 37 (74 %). There were 1 flow limiting dissection requiring stent deployment and 3 non-cardiovascular related deaths in 6 months follow-up. No records of perforation, no-reflow or other major adverse events. CONCLUSION: Among patients with heavy calcified lesions undergoing OCT guided intervention with OPN NC, acceptable expansion was achieved in most cases without procedure related complications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".