Computed Tomographic Angiography as the Primary Diagnostic Study in Spontaneous Subarachnoid Hemorrhage
Bibliographic record
Abstract
PURPOSE: To evaluate the utility of computed tomographic angiography (CTA) as the primary diagnostic investigation in patients with spontaneous subarachnoid hemorrhage (SAH), and to correlate the results with intraoperative findings in those with ruptured aneurysms. MATERIALS AND METHODS: A retrospective review of 243 patients with spontaneous SAH was performed. The patients selected were those with acute SAH confirmed by noncontrast head computed tomography or by cerebrospinal fluid findings from a lumbar puncture. Patients subsequently underwent preoperative three-dimensional CTA as the sole or primary diagnostic study. The results of the CTA were correlated with the intraoperative findings in those patients undergoing emergent surgical clipping of acutely ruptured intracranial aneurysms. RESULTS: CTA correctly detected the ruptured aneurysm in 170 of the 171 cases, which required surgical clipping. Our data demonstrates that CTA has a 99.4% detection rate in acutely ruptured aneurysms as compared to intraoperative findings [confidence interval 97.8-99.9%]. CONCLUSION: CTA can provide prompt and accurate diagnostic and anatomic information in the setting of SAH with an excellent detection rate in acute ruptured aneurysms. These findings suggest an increased role for CTA in the evaluation of cerebral 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 | 0.002 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".