Balloon Dilatation and Stenting for Aortic Coarctation
Bibliographic record
Abstract
BACKGROUND: There is no systematic assessment of available evidence on effectiveness and comparative effectiveness of balloon dilatation and stenting for aortic coarctation. METHODS AND RESULTS: We systematically searched 4 online databases to identify and select relevant studies of balloon dilatation and stenting for aortic coarctation based on a priori criteria (PROSPERO 2014:CRD42014014418). We quantitatively synthesized results for each intervention from single-arm studies and obtained pooled estimates for relative effectiveness from pairwise and network meta-analysis of comparative studies. Our primary analysis included 15 stenting (423 participants) and 12 balloon dilatation studies (361 participants), including patients ≥10 years of age. Post-treatment blood pressure gradient reduction to ≤20 and ≤10 mm Hg was achieved in 89.5% (95% confidence interval, 83.7-95.3) and 66.5% (44.1-88.9%) of patients undergoing balloon dilatation, and in 99.5% (97.5-100.0%) and 93.8% (88.5-99.1%) of patients undergoing stenting, respectively. Odds of achieving ≤20 mm Hg were lower with balloon dilatation as compared with stenting (odds ratio, 0.105 [0.010-0.886]). Thirty-day survival rates were comparable. Numerically more patients undergoing balloon dilatation experienced severe complications during admission (6.4% [2.6-10.2%]) compared with stenting (2.6% [0.5-4.7%]). This was supported by meta-analysis of head-to-head studies (odds ratio, 9.617 [2.654-34.845]) and network meta-analysis (odds ratio, 16.23, 95% credible interval: 4.27-62.77) in a secondary analysis in patients ≥1 month of age, including 57 stenting (3397 participants) and 62 balloon dilatation studies (4331 participants). CONCLUSIONS: Despite the limitations of the evidence base consisting predominantly of single-arm studies, our review indicates that stenting achieves superior immediate relief of a relevant pressure gradient compared with balloon dilatation.
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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 | 0.021 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".