Comparison of CTA to DSA in Determining the Etiology of Spontaneous ICH
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To compare the efficacy of computed tomographic angiography (CTA) to that of digital subtraction angiography (DSA) in the detection of secondary causes of intracerebral hemorrhage (ICH). METHODS: Between January 2001 and February 2007 there were 286 patients that had both CTA and DSA for intracranial hemorrhage of all types. Those with primarily subarachnoid hemorrhage or recent trauma were excluded. Fifty-five patients formed the study cohort. Three reviewers independently analyzed the CTAs in a blinded protocol and classified them based on presence or absence of a secondary etiology. Results were compared with the reference standard DSA and kappa values determined for interobserver variability. RESULTS: The overall sensitivity, specificity, positive predictive value, negative predictive value and accuracy of CTA were 89%, 92%, 91%, 91% and 91%, respectively. Kappa value for interobserver agreement ranged from 0.78 to 0.89. Two of four dural arteriovenous fistulas (dAVF) were missed on CTA by all three reviewers. CONCLUSION: CTA is nearly as effective as DSA at determining the cause of secondary intracerebral hemorrhage, but with a lower sensitivity for dAVFs. This supports the use of CTA as the first screening test in patients presenting with spontaneous ICH.
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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 | 0.013 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| 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".