Influence of neurovascular embolic coil primary wind diameter on aneurysm packing density and case costs
Bibliographic record
Abstract
AIMS: Endovascular coiling is a common modality for treating intracranial aneurysms; however, recanalization occurs in approximately 1 in 5 cases, with downstream consequences of regrowth and rupture. Aneurysm packing density >24% reduces recanalization risk; packing density can be increased by inserting additional coils or by using coils with larger volumetric filling. Coil volume depends on length and primary wind diameter (PWD). This study evaluated the influence of PWD on packing density and total case costs. MATERIALS AND METHODS: Two hypothetical scenarios and one case study were analyzed. In scenario one, the number of coils required to achieve packing density >24% in a hypothetical aneurysm was determined for 0.012″ vs. 0.010″ PWD coils. In scenario two, the total length of 0.010″ vs. 0.012″ PWD coils required to achieve a packing density >24% was analyzed relative to aneurysm volume. In the case study, packing densities with one 0.012″ PWD coil (actual scenario) and one 0.010″ PWD coil (theoretical scenario) were compared. RESULTS: In scenario one, cost savings would be realized by using four 0.012″ PWD coils vs. seven 0.010″ PWD coils to achieve packing density >24%. In scenario two, greater volumetric filling of 0.012″ vs. 0.010″ PWD coils was correlated with lower total length of coil required. In the case study, a 0.012″ PWD coil achieved packing density >24%, whereas an equivalent length 0.010″ PWD coil would not. LIMITATIONS: Theoretical modeling was used to explore the impact of coil PWD on aneurysm packing density. In clinical practice, packing density depends not only on PWD but on its length, shape, distribution within an aneurysm, and other recanalization risk factors. CONCLUSIONS: Coil PWD influences packing density, the number of coils required to achieve a specific packing density, and total case costs. Using 0.012″ PWD coils may provide cost and procedural efficiencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".