195 * AORTIC VALVE REPAIR FOR INSUFFICIENCY IN OLDER CHILDREN OFFERS UNPREDICTABLE DURABILITY THAT MAY NOT BE ADVANTAGEOUS OVER PRIMARY ROSS OPERATION
Bibliographic record
Abstract
Objectives: To evaluate the durability of primary aortic valve repair versus Ross procedure or aortic valve replacement (AVR) in children with severe aortic insufficiency (AI). Methods: From 2001 to 2012, 90 children with severe AI underwent repair (n = 46, 51%), Ross (n = 21, 23%) or AVR (n = 23, 26%). Repeated measures (n = 1081 echoes) mixed model analysis was used to evaluate haemodynamic outcomes. Results: Mean age at operation was identical for repair and Ross (11 years), but slightly older for AVR (13 years); however, annular dimensions were similar across the three groups (Fig. 1). Cardiopulmonary bypass times were significantly shorter for repairs versus either Ross or AVR (Fig. 1, P < 0.01). Repairs were initially attempted in 52; 6 switched strategy during surgery to Ross (5) or AVR (1). Partly as a consequence, repairs, therefore, were associated with more multiple bypass runs than primary strategies of Ross or AVR. Need for multiple runs did not influence survival or reoperation. After repair, peak left ventricular outflow tract (LVOT) pressure gradients were highly variable (Fig. 1). Some offered reasonable durability with a peak LVOT gradient of ∼50 mmHg at 5–8 years; however, recurrent stenosis was often evident within 2–3 years (Fig. 1). There have been no deaths. Estimated freedom from surgical reoperation is 74%, 100% and 63% at 5 years for repairs, Ross and AVR, respectively (P = 0.05). Four of 5 reoperations after AVR were for failed non-mechanical devices. Conclusion: Durability after aortic valve repair for AI in children is unpredictable and this strategy may offer little benefit over primary Ross.
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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) | 0.000 | 0.001 |
| 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) | 0.003 | 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".