Dilatation of the ascending aorta in paediatric patients with bicuspid aortic valve: frequency, rate of progression and risk factors
Bibliographic record
Abstract
OBJECTIVES: To describe the incidence and rate of dilatation of the ascending aorta in children with bicuspid aortic valve (BAV) and to determine factors that predict rapid aortic dilatation. DESIGN: Retrospective cohort study. SETTING: Regional tertiary care children's hospital. PATIENTS: All children aged 0-18 years seen at the authors' institution between 1990 and 2003 with an "isolated" BAV. All patients had had more than one technically adequate echocardiogram, at least six months apart, with concomitant height and weight data. INTERVENTIONS: Offline echocardiographic measurements of multiple levels of the aortic root were completed for each participant at each serial echocardiogram. These measurements were then compared with expected measurements derived from a normal local control population. MAIN OUTCOME MEASURES: Rate of change of the ascending aorta size over time, where aortic size is expressed as the number of standard deviations above or below the mean size expected for a given body surface area (z score). RESULTS: 279 echocardiograms spanning a period of from 9 months to 13.3 years were analysed for 88 patients with BAV. The ascending aorta in the BAV group was larger than expected for body surface area at diagnosis and continued to increase in relative size at each of the four subsequent follow-up echocardiograms. Ascending aortic z score increased at an average rate of 0.4/year. A faster rate of increase in z score was predicted by both larger initial aortic valve gradient and non-use of beta blockers. CONCLUSIONS: Children with BAV are at risk of having a dilated ascending aorta. This risk increases with longer follow up.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".