Small Number of Coils With Extended Length in the Endovascular Treatment of Cerebral Aneurysm: Experience of 108 Cases in a Single-Center
Bibliographic record
Abstract
Background: Knowledge about the number and packing density of coils for aneurysm treatment affects the efficiency of health services and hospital financing. This study aimed to report case series with a small number of coils with longer lengths and impact on packing density. Methods: We retrospectively analyzed the morphology of aneurysms (location and size) and the data of characteristic features of coils (numbers, diameter size, length, and packing density) from the cerebral aneurysm registry at a single center from January 2019 to June 2022. Kruskal-Wallis analysis was performed to identify the association between coil characteristics based on the size of the aneurysm. Results: Of the 108 patients included in the study, 116 aneurysms were identified and coiled; 66 were small (< 5 mm), 42 were moderate (5 - 10 mm), and eight were large (> 10 mm). Most cases utilized framing first coil (in 64.65%) and helical coils (in 31.89%). The total length of framing coils (< 5 mm: 6.76 ± 4.14 cm, 5 - 10 mm: 11.28 ± 6.93 cm, > 10 mm: 29.33 ± 12.59 cm) and helical coils (< 5 mm: 5.95 ± 2.82 cm, 5 - 10 mm: 11.66 ± 7.3 cm, > 10 mm: 22.32 ± 11.48 cm) also increased with group size. The highest packing density (46.70%) was achieved in small aneurysms (< 5 mm). Conclusion: In small aneurysm, one to three coils per aneurysm were required to achieve tight coil packing density by extending the length of coil. J Neurol Res. 2024;14(1):1-7 doi: https://doi.org/10.14740/jnr777
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".