Anatomic and procedural determinants of fluoroscopy time during elective endovascular aortic aneurysm repair
Bibliographic record
Abstract
OBJECTIVE: To identify both the procedural and anatomic factors which determine duration of fluoroscopy during elective endovascular aortic aneurysm repair (EVAR). METHODS: We retrospectively analyzed our prospectively maintained EVAR database for the relationship between fluoroscopy time and both procedural (type of graft, configuration, number of components, surgeon) and anatomic factors reflective of aneurysm complexity (15 variables). RESULTS: A total of 128 patients underwent elective EVAR with a mean fluoroscopy time of 5.7 ± 3.4 min. The type of grafts used consisted of 41 (32%) Zenith, 85 (66.4%) Endurant and 2 (1.6%) Anaconda, with 105 (82%) being bifurcated and 23 (18%) being aorto-uni-iliac (AUI) in configuration. Both the surgeon performing the procedure (p = 0.001) and graft configuration (bifurcated vs. AUI, p = 0.03) were found to be predictive of fluoroscopy time; while procedural and anatomic variables were not. CONCLUSIONS: The surgeon's efficiency in the use of fluoroscopy during EVAR is the most important determinant of total fluoroscopy time. Anatomic complexity, make of device, and number of components inserted have minimal impact on duration of fluoroscopy. An endovascular surgeon's ability to curtail fluoroscopy duration is the key component in minimizing radiation exposure to both the surgical team and the patient.
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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 | 0.006 |
| 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 | 0.000 |
| 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".