320-Row Multidetector CT Angiography in the Detection of Critical Cerebrovascular Anomalies
Bibliographic record
Abstract
BACKGROUND: The acquisition of a new 320-row multidetector computed tomography angiography (CTA) scanner at the Montreal Neurological Institute and Hospital has provided higher quality imaging with less radiation exposure and shorter time of acquisition. However, its reliability has not been fully proven in critical vascular lesions when it comes to replacing a more invasive examination such as cerebral angiography. We wished to validate the accuracy of this equipment to investigate four common indications for patients to undergo conventional digital subtraction angiography: subarachnoid hemorrhage, vasospasm, unusual intracerebral hemorrhage, and unruptured aneurysm. METHODS: Radiological reports and relevant imaging from 82 consecutive subjects who underwent a 320-row multidetector CTA followed by cerebral angiography from February 2010 to February 2014 were retrospectively analysed. A total of 102 cerebrovascular anomalies were found. Reports from both imaging modalities were compared to determine the diagnostic accuracy of CTA. RESULTS: The overall sensitivity and specificity of 320-row multidetector CTA for detecting cerebrovascular abnormalities were, respectively, 97.60% and 63.20%. Similar results were obtained for all four categories of clinical indications. CONCLUSION: Results obtained from CTA were consistent with those obtained on digital subtraction angiography regardless of the vascular pathology. To our knowledge, this study is the first validating the accuracy of 320-row CTA in diagnosing critical cerebrovascular lesions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".