New Pipeline Flex device: initial experience and technical nuances
Bibliographic record
Abstract
BACKGROUND: Flow diverter stents (FDS) have been described as a breakthrough in the treatment of intracranial aneurysms. Of the various flow diverter models, the Pipeline device has been the main approved and used device, with established and good long-term results. OBJECTIVE: To present the first series of patients treated with its new version, the Pipeline Flex device. This has kept the same device design and configuration but redesigned and completely modified the delivery system. METHODS: In this technical report, we include 10 consecutive patients harboring 12 saccular aneurysms of the anterior circulation. We report the main changes on the system, immediate results, and technical nuances with illustrative cases. RESULTS: We implanted 12 devices, including 11 Pipeline Flex and one Pipeline device. We used the old version in one case that required a second layer with a short length not available in the Pipeline Flex size range. All attempts at treatment were successful and no device was discharged or removed. Recovery was required or used in half of the cases with good or excellent performance, except in one case that presented with multiple proximal loops and tight curves. We had two transitory events without ischemic lesions on MRI that recovered 1 and 4 h after all patients were discharged home asymptomatic. CONCLUSIONS: Pipeline Flex represents a major advance in FDS technology. The redesigned system has significantly improved the deployment of the Pipeline stent, by enabling the operator to resheath the device. It has the potential to continue revolutionizing the endovascular approach for intracranial aneurysms.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".