CT Angiography Alone Can Provide Sufficient Angio-Architectual Information About Unruptured Intracranial Aneurysms to Proceed Straight to Treatment (P1.145)
Bibliographic record
Abstract
Background: The prevalence of intracranial aneurysms is estimated to be between 0.2 and 9.9% for the general population, with an average annual rupture rate of up to 1.3%. Unruptured intracranial aneurysms (UIA) pose a dilemma to clinicians attempting to ascertain the risk of rupture and balance that with treatment associated complications. Traditionally digital subtraction angiography (DSA) has been used as the gold standard for treatment planning. Evolving CT angiography (CTA) technology has resulted in a paradigm shift towards using CTA as the sole modality for pre-treatment decision making. Method: We retrospectively analyzed 29 consecutive patients with UIA who underwent either endovascular coiling or neurosurgical clipping at our institution from January 2013 to July 2013. All patients had unruptured aneurysms imaged on CTA. Results: For 22 patients (76%) the CTA alone was deemed sufficient to proceed straight to treatment, while for 7 patients (24%), DSA was considered necessary for treatment planning. Of the 22 patients who went straight to treatment, 19 were successfully treated (17 patients had endovascular coiling and 2 patients underwent surgical clipping). 3 patients (10%) in the straight to treatment group failed an attempt at endovascular coiling due to unfavorable anatomy. These patients required neurosurgical clipping. Conclusion: 76% of our patients did not undergo DSA prior to treatment. Information provided by CTA was sufficient to treat 19 of the 22 patients (86%). The use of routine DSA for the pre-treatment planning of unruptured intracranial aneurysms may warrant reconsideration in centers with experience in neurovascular CTA.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".