F.04 Flow diversion in the treatment of aneurysms: A randomized care trial and registry
Bibliographic record
Abstract
Background: The Flow diversion in the treatment of Intracranial Aneurysm (FIAT) trial was designed to guide the clinical use of flow diversion. Methods: FIAT proposed randomized allocation flow diversion or standard management (observation, coiling, parent vessel occlusion, or clipping), and a registry of non-randomized patients treated with flow diversion. Primary safety outcome was death or dependency (mRS > 2) at 3 months. Primary efficacy outcome was angiographic occlusion at 3-12 months combined with independent clinical outcome. Results: Of 112 participating patients recruited, 78 were randomized, and 34 received flow diversion within the registry. The study was halted for safety concerns. Twelve of 73 patients (16.4%; CI [9.7% -26.7%]) who were allocated or received flow diversion at any time were dead (n=8) or dependent (n=4) at 3 months or more, crossing a predefined safety boundary. Death or dependency occurred in 5 of 36 patients randomly allocated flow diversion and in 5 of 36 patients allocated standard treatment (13.9%; [6.1%-28.7%]). Efficacy was below hypothesized expectations: 15 of 36 patients (41.7%; [27.1%-57.8%]) randomly allocated flow diversion failed to reach the primary outcome, as compared to 11 of 36 patients allocated standard treatment (30.1%; [18.0%-46.9%]). Conclusions: Flow diversion was not as safe and effective as hypothesized. More randomized trials are needed.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".