The impact of randomized trial results on abdominal aortic aneurysm repair rates from 2003 to 2016: A population-based time-series analysis
Bibliographic record
Abstract
Objectives The uptake of endovascular aortic repair for elective and ruptured abdominal aortic aneurysm repair is not well studied. We aimed to examine the trends in open surgical repair and endovascular aortic repair of eAAA and rAAA and to examine the effects of randomized trial publications on elective open surgical repair and endovascular aortic repair rates. Methods We conducted a population-based time-series analysis of eAAA and rAAA repairs in Ontario, Canada from 2003 to 2016. We examined changes in overall and approach-specific rates of eAAA and rAAA repair using exponential smoothing models. Interventional autoregressive integrated moving average models were fit to the eAAA rates to examine the impact of randomized trial results on these rates. Results We identified 19,489 eAAA (12,232 open (63%) and 7257 endovascular (37%)) and 2732 rAAA (2466 open (90%) and 266 endovascular (10%)) repairs from 2003 to 2016. The rate of eAAA repair declined from 6.39/100,000 in 2003 to 5.59/100,000 in 2016 (13% decrease, p = 0.17). The rate of elective open surgical repair decreased nearly three-fold from 6.07/100,000 to 2.12/100,000 ( p < 0.0001), while elective endovascular aortic repair increased approximately 10-fold (0.32/100,000 to 3.47/100,000, p < 0.0001). The rate of ruptured open surgical repair decreased from 1.62/100,000 to 0.37/100,000 ( p < 0.44), while ruptured endovascular aortic repair uptake increased (0.00/100,000 to 0.12/100,000, p < 0.25). The mid-term results of the DREAM and EVAR-1 trials were associated with a decrease in the rate of elective open surgical repair decline after 2010 ( p = 0.01). Conclusions While elective open surgical repair use has significantly decreased from 2003 to 2016, elective endovascular aortic repair use has significantly increased. The DREAM and EVAR-1 results significantly impacted the observed rates of elective open surgical repair only. The reasons for these trends require further characterization.
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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 | 0.197 | 0.414 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".