Minilaparotomy for Aortoiliac Aneurysmal Disease
Bibliographic record
Abstract
Vascular surgery is evolving, as other specialities, toward minimally invasive techniques. Presently, 3 approaches to aortoiliac disease are suggested as minimally invasive. Besides the endovascular procedures, laparoscopic techniques and minilaparotomy are being advocated. Although for aneurysmal disease, we favor a totally laparoscopic approach, criticisms raised over laparoscopy-assisted techniques by those advocating minilaparotomy led us to investigate the benefits of the latter technique. We first evaluated the procedure in 7 patients with infrarenal abdominal aortic aneurysm (AAA). We found the procedure impossible to perform with an 8- to 10-cm incision in 6 of the 7 patients. This led us to evaluate causes of failure of the technique. It appeared to us that most of our complications were related to inadequate exposure. Fifty consecutive computed tomography scans from patients with AAA of surgical size were then reviewed to evaluate the aneurysm lengths and compare them to the reported lengths of skin incision for minilaparotomy. Results were expressed adding a total of 2 cm for proximal and distal clamping. Only 2% of patients would present with aneurysms suitable for treatment through an 8-cm midline incision and 30% through a 10-cm incision. We then reviewed the literature on minilaparotomy. We believe that minilaparotomy should be reserved for those patients with purely aortic disease and the appropriate body habitus.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".