Digital Subtraction Angiography for CT Angiogram Negative Haemorrhages
Bibliographic record
Abstract
BACKGROUND: In patients with subarachnoid haemorrhage (SAH) and a negative finding on CT angiography (CTA), further imaging with digital subtraction angiography (DSA) is commonly performed to identify the source of bleeding. The purpose of this study was to investigate whether negative findings on CTA can reliably exclude aneurysms in patients with acute SAH. METHODS: This retrospective study identified all DSAs performed between August 2010 and July 2014 within our institution. CT angiography was performed with a 64-section multidetector row CT scanner. Only DSAs from patients with confirmed SAH and a negative CTA result were included in the final analyses. A fellowship-trained neuroradiologist reviewed the imaging results. RESULTS: Of the 857 DSAs, 50 (5.83%) were performed in 35 patients with CTA-negative SAH. Of the 35 patients, three (8.57%) had positive findings on the DSA. In one patient, suspicious dissection of the extra- and intra-cranial segment of the right vertebral artery could not be confirmed even in retrospect. In the second patient, the suspicious finding of tiny protuberance from the left paraclinoid internal carotid artery (ICA) on DSA did not change on follow-up and did not change patient's management. The third patient had a posterior inferior cerebellar artery aneurysm, which was not seen on the initial CTA owing to the incomplete coverage of the head on the CTA. CONCLUSION: In patients with SAH, negative findings on a technically sound CTA are reliable in ruling out aneurysms in any pattern of SAH or no blood on CT. Our observations need to be confirmed with larger prospective studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".