Anterior communicating artery aneurysm: Accuracy of CT angiography in determination of inflow dominance
Bibliographic record
Abstract
BACKGROUND: Preoperative assessment of anterior communicating artery (AcoA) aneurysms with cerebral angiography is common, but not without risk. Computed tomography angiography (CTA) is a widely available imaging modality that provides quick acquisition, low morbidity, and low cost. One disadvantage is that it does not provide dynamic information. In this study, the authors sought to determine whether CTA alone can reliably predict the inflow dominance to an AcoA aneurysm. METHODS: Eighty-three patients with ruptured AcoA aneurysms were reviewed retrospectively. Only those patients with both preoperative CTA and cerebral angiogram were included, thus excluding six patients. Four independent observers reviewed the CTAs and attempted to identify the dominant A1. Additionally, three mathematical models were created to identify the dominant A1. These responses were compared to cerebral angiograms. RESULTS: Four observers were correct in judging the dominant A1 an average of 93% of the time. Seventeen cases were read incorrectly by only one of four observers, and three cases were read incorrectly by two observers. For cases with incorrect readings, the average percentage difference in A1 sizes was 19.6%. For cases read unanimously correct, the average percentage difference in A1 sizes was 42.7%. Mathematical model #3 correctly evaluated the dominant A1 in 97% of the cases. CONCLUSIONS: This study found CT angiograms can be reliable in predicting the inflow dominance to the majority of AcoA 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 | 0.004 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".