Flow Diversion in Aneurysms Trial: The Design of the FIAT Study
Bibliographic record
Abstract
Intracranial aneurysms, particularly large and giant, fusiform or recurrent aneurysms are increasingly treated with flow diverters (FDs), a recently introduced and approved neurovascular device. While some rare cases may not be treated any other way, in most patients a more conventional, conservative, or validated approach such as coiling, parent vessel occlusion, or surgical clipping exists. Only a randomized clinical trial can answer the question of which treatment option leads to better patient outcomes.We report the design of the FIAT study, a clinical care trial aiming to compare angiographic and clinical outcomes following treatment with a Flow-Diverter or with the best conventional treatment option. The FIAT study will include both a randomized and a registry portion. Patients will be proposed randomization to either FD stenting or best conventional treatment option (observation, coiling, stenting, or clipping) as determined by the treating physician. FIAT will recruit a total of 338 patients, to show that i) FD stenting can be performed with an 'acceptable' immediate complication rate of less than 15% morbidity and mortality (defined as mRS > 2); ii) FD stenting can increase from 75 to 90% the proportion of patients with a "good outcome", defined as complete or near-complete occlusion of the aneurysm AND a good clinical outcome (mRS ≥ 2) at one year, as compared to the best conventional option. The FIAT study provides a scientific and ethical context to care for patients eligible for flow-diversion therapy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".