Hydrogel versus Bare Platinum Coils in Patients with Large or Recurrent Aneurysms Prone to Recurrence after Endovascular Treatment: A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Some patients are at high risk of aneurysm recurrence after endovascular treatment: patients with large aneurysms (Patients Prone to Recurrence After Endovascular Treatment PRET-1) or with aneurysms that have previously recurred after coiling (PRET-2). We aimed to establish whether the use of hydrogel coils improved efficacy outcomes compared with bare platinum coils. MATERIALS AND METHODS: PRET was an investigator-led, pragmatic, multicenter, parallel, randomized (1:1) trial. Randomized allocation was performed separately for patients in PRET-1 and PRET-2, by using a Web-based platform ensuring concealed allocation. The primary outcome was a composite of a residual/recurrent aneurysm, adjudicated by a blinded core laboratory, or retreatment, intracranial bleeding, or mass effect during the 18-month follow-up. Secondary outcomes included adverse events, mortality, and morbidity (mRS > 2). The hypothesis was that hydrogel would decrease the primary outcome from 50% to 30% at 18 months, necessitating 125 patients per group (500 for PRET-1 and PRET-2). RESULTS: The trial was stopped once 250 patients in PRET-1 and 197 in PRET-2 had been recruited because of slow accrual. A poor primary outcome occurred in 44.4% (95% CI, 35.5%–53.2%) of those in PRET-1 allocated to platinum compared with 52.5% (95% CI, 43.4%–61.6%) of patients allocated to hydrogel (OR, 1.387; 95% CI, 0.838–2.295; P = .20) and in 49.0% (95% CI, 38.8%–59.1%) in PRET-2 allocated to platinum compared with 42.1% (95% CI, 32.0%–52.2%) allocated to hydrogel (OR, 0.959; 95% CI, 0.428–1.342; P = .34). Adverse events and morbidity were similar. There were 3.6% deaths (1.4% platinum, 5.9% hydrogel; P = .011). CONCLUSIONS: Coiling of large and recurrent aneurysms is safe but often poorly effective according to angiographic results. Hydrogel coiling was not shown to be better than platinum.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".