Results of a multimodal approach for the management of aortic coarctation and its complications in adults
Bibliographic record
Abstract
OBJECTIVES: We aimed to assess the results of various tailored management strategies for adults with coarctation in our centre. METHODS: We reviewed all adults patients treated for aortic caorctation between January 2000 and December 2015 in our institution. The primary end point was a composite of death, perioperative stroke, paraplegia, need for unplanned reoperation or occurrence of pseudoaneurysm during the follow-up. The mean follow-up was 82 ± 5 months. RESULTS: Sixty-three adults were treated for a native coarctation (n = 34), a recurrent coarctation (n = 14) or aneurysmal complication (n = 15). Mean age of the patients was 42 ± 1.7 years. All but 1 patient with native coarctation (33/34, 97%) and recurrent coarctation (13/14, 93%) underwent endovascular repair and 10 (67%) patients with aneurysmal complications were treated surgically. Freedom from the primary composite end point was 94, 84 and 81% at 1, 5 and 10 years, respectively, without difference between the 3 indication groups (P = 0.96). CONCLUSIONS: A tailored management strategy is necessary to provide good results for the treatment of adults with aortic coarctation. Thus, centres that are involved in the care of this complex pathology should be able to propose a multimodal approach, either endovascular or surgical depending on patient's characteristics and anatomic features.
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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 | 0.000 |
| 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) | 0.001 | 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".