Thoracic stent grafts
Bibliographic record
Abstract
Abstract Background The Endovascular Aneurysm Repair trial of infrarenal aortic aneurysm repair requires 20 operations to be performed at each centre to eliminate learning curve errors. An analysis was undertaken of the authors' learning curve experience of commercially made thoracic stent grafts. Methods Stent grafting was attempted in 26 patients. Aortic pathology included 13 atheromatous aneurysms, six dissections, one coarctation, three false aneurysms and three transections. Successful deployment was performed in 24 patients (92 per cent), but failed in two women due to small iliac arteries. Stent grafts used were Gore Excluder (16 patients), AneuRx (six), Vanguard (one) and Stenford (one). Four patients required two stents, two needed three stents, and 18 had a single stent. Results Thirteen elective procedures were uneventful. Two deaths occurred in 11 urgent procedures, from pulmonary embolism and aortic rupture of an unsuspected false aneurysm. The overall in-hospital mortality rate was 8 per cent (two of 24 patients). One graft with a persistent endoleak was removed at open repair at 6 weeks. The subclavian artery origin was covered in three elderly patients, resulting in minor distal ischaemia. No spinal cord problems were seen. One patient died from pneumonia at 8 weeks, and another died from rupture at 28 months as a result of prolapse of the stent into the aneurysm sac. Conclusion Assessment of the diameter of the iliac arteries is important, especially in women, to ensure that they accommodate the size of the sheath. Patients with false aneurysms have a poor outcome, and treatment by stent grafting may not be durable. Covering the origin of the left subclavian artery can be undertaken in elderly patients with transient minor symptoms. The use of stent grafts in acute type B dissection should be the subject of a randomized trial. Continued surveillance is essential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.015 |
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".