Population–based Long–term Trends and Outcomes of Endovascular Versus Open Aortic Repair of Abdominal Aortic Aneurysms
Bibliographic record
Abstract
Introduction: Knowledge regarding the long–term outcomes of endovascular aortic repair is growing. However, data with more than 10 years follow–up remain sparse, and are lacking from population–based studies. This thesis sought to determine the long–term outcomes of endovascular versus open surgical repair of both elective and ruptured abdominal aortic aneurysms. Methods: A chart re–abstraction study was conducted to validate open and endovascular abdominal aortic aneurysm repair coding in Ontario administrative health data from 2003 to 2015. The algorithm was used to identify cohorts of elective and ruptured open and endovascular abdominal aortic aneurysm patients from 2003 to 2016. The temporal trends in the two repair approaches were analyzed using time–series analysis. Propensity score methods and survival analyses were used to determine the long–term outcomes of endovascular versus open repair of abdominal aortic aneurysm in elective and ruptured settings. Results: Administrative data codes identified open repairs with greater than 87% positive predictive value, and endovascular repairs with greater than 91% positive predictive value. Two thirds of elective and 90% of ruptured aneurysm repairs were by the open approach. The rate of elective open repair decreased nearly three–fold (P
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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 | 0.004 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".