The Pipeline Flow-Diverting Stent for Exclusion of Ruptured Intracranial Aneurysms With Difficult Morphologies
Bibliographic record
Abstract
BACKGROUND: The Pipeline Embolization Device (PED) is a flow-diverting stent that may represent a new therapeutic tool for difficult-to-treat intracranial aneurysms, including those that present with subarachnoid hemorrhage (SAH). OBJECTIVE: To demonstrate the feasibility of utilizing the PED as a primary treatment for ruptured aneurysms with challenging morphologies. METHODS: Three patients with ruptured intracranial aneurysms presented with SAH. Three distinct and difficult-to-treat aneurysm morphologies were encountered: (1) a small basilar trunk pseudoaneurysm, (2) a carotid artery blister aneurysm, and (3) an A1/A2 junction-dissecting-type aneurysm. All were treated with deployment of one or more PEDs across the aneurysm. RESULTS: PEDs were successfully deployed in all 3 cases. Two patients were treated with 2 overlapping PEDs, and the third patient was treated with a single device. Aneurysm obliteration was achieved in all 3 cases with no early rehemorrhage or other clinically adverse event. CONCLUSION: Endovascular treatment with the pipeline flow-diverting stent may be a viable treatment option for otherwise difficult-to-treat aneurysm morphologies in the context of acute SAH.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".