Building Multidevice Pipeline Constructs of Favorable Metal Coverage: A Practical Guide
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The advent of low-porosity endoluminal devices, also known as flow diverters, exemplified by the Pipeline in the United States, produced the greatest paradigm shift in cerebral aneurysm treatment since the introduction of detachable coils. Despite robust evidence of efficacy and safety, key questions regarding the manner of their use remain unanswered. Recent studies demonstrated that the Pipeline device geometry can dramatically affect its metal coverage, emphasizing the negative effects of oversizing the device relative to its target vessels. This follow-up investigation focuses on the geometry and coverage of multidevice constructs. MATERIALS AND METHODS: A number of Pipeline devices were deployed in tubes of known diameters and photographed, and the resultant coverage was determined by image segmentation. Multidevice segmentation images were created to study the effects of telescoped devices and provide an estimate of coverages resulting from device overlap. RESULTS: Double overlap yields a range of metal coverage, rather than a single value, determined by the diameters of both devices, the size of the recipient artery, and the degree to which strands of the overlapped devices are coregistered with each other. The potential variation in coverage is greatest during overlap of identical-diameter devices, for example, ranging from 24% to 41% for two 3.75-mm devices deployed in a 3.5-mm vessel. Overlapping devices of progressively different diameters produce correspondingly more uniform ranges of coverage, though reducing the maximum achievable value, for example, yielding a 33%-34% range for 3.75- and 4.75-mm devices deployed in the same 3.5-mm vessel. CONCLUSIONS: Rational strategies for building multidevice constructs can achieve favorable geometric outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".