The design of the STenting in Aneurysm Treatments (STAT) trial
Bibliographic record
Abstract
UNLABELLED: Unruptured intracranial aneurysms (UIA) are increasingly treated with endovascular treatment although this method continues to be associated with aneurysm recurrences in up to 30-40%, especially for large aneurysms or those with wide (>4 mm) necks. Although the significance of a recurrence remains unknown, they not only require angiographic follow-up but discovery sometimes leads to retreatment, with associated risks. Several strategies have been developed to decrease recurrence rates, including the addition of an endovascular stent to standard coiling. Stents may permit more complete coil occlusion, form a neointimal scaffold at the aneurysm neck and normalize blood flows. A randomized study of endovascular treatment of UIAs for aneurysms treated with or without stenting has not been performed. The design of the STenting in Aneurysm Treatments (STAT) trial is reported, which compares angiographic and clinical outcomes following endovascular treatment of UIAs with or without stents. The first phase of this pragmatic management trial will examine angiographic outcomes, in order to determine whether the addition of a stent to standard coiling can decrease recurrence rates, while the second phase of the study will determine if stenting is associated with increased patient morbidity and mortality. The STAT trial collaborators intend to enroll 600 patients, a size sufficient (at 80% power and 0.05 significance) to detect a decrease in recurrences from 33% to 20% by 1 year, and to verify that stenting does not result in an increase in the proportion of patients experiencing neurological disability (modified Rankin Scale score >2), from 6% to 12%. TRIAL REGISTRATION NO: ClinicalTrials.gov Identifier: NCT01340612.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".