Exploring the Age of Intracranial Aneurysms Using Carbon Birth Dating
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: There is a controversy about the time span over which cerebral aneurysms develop. In particular, it is unknown whether collagen in ruptured aneurysms undergoes more rapid turnover than in unruptured aneurysms.(14)C birth dating of collagen could be used to address this question. METHODS: Aneurysmal domes from patients undergoing surgical treatment for ruptured or unruptured aneurysms were excised. Aneurysmal collagen was isolated and purified after pepsin digestion. Collagen from mouse tendons served as controls. F(14)C levels in collagen were analyzed by accelerator mass spectrometry and correlated with patient age and aneurysm size. RESULTS: Analysis of 10 aneurysms from 9 patients (6 ruptured, 3 unruptured) revealed an average aneurysm collagen age of <5 years, generally irrespective of patient age and aneurysm size or rupture status. Interestingly, F(14)C levels correlated with patient age as well as aneurysm size in ruptured aneurysm collagen samples. CONCLUSIONS: Our preliminary data suggest that collagen extracted from intracranial aneurysms generally has a high turnover, associated with aneurysm size and patient age. The correlation of patient age and aneurysm F(14)C levels could explain models of aneurysm development. Although preliminary, our findings may have implications for the biological and structural stability of ruptured and unruptured 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".