Morphological and Hemodynamic Effectiveness of Stenting for Pulmonary Artery Stenosis – Subanalysis of JPIC Stent Survey –
Bibliographic record
Abstract
BACKGROUND: Percutaneous stenting for branch pulmonary artery stenosis is an established interventional choice in congenital heart disease. The apparent morphologic change in the vessel diameter often differs from the hemodynamic result. METHODS AND RESULTS: We performed a subanalysis of the data from the Japanese Society of Pediatric Interventional Cardiology (JPIC) stent survey. The factors that may have contributed to morphologic effectiveness included reference vessel diameter (RVD), minimum lumen diameter (MLD) and percent diameter stenosis (%DS) and the relation between morphologic and hemodynamic effectiveness was evaluated in 206 lesions treated with stenting. We defined a "50% increase in MLD" as "morphologically effective", while "achievement of either a reduced pressure gradient greater than 50% or an increase of perfusion ratio to the affected side to the contralateral side greater than 20%" as "hemodynamically effective". Morphologic effectiveness was achieved in 84% of patients. Before stenting, %DS was significantly larger, while RVD was smaller in the "effective" group than in the "non-effective" group. The cutoff value for effective stenting was 51% for %DS and 14.7 mm for RVD before stenting. Hemodynamic effectiveness was obtained more often in the "morphologic effective" group. CONCLUSIONS: RVD and %DS were the 2 main contributors to acute morphologic effectiveness. There was a significant relationship between "morphologic effectiveness" and "hemodynamic effectiveness", judging from increased perfusion of the affected lung and/or decreased pressure gradient. (Circ J 2016; 80: 1852-1856).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".