Detection and Measurement of Intracranial Aneurysm Compared between Magnetic Resonance Intracranial Black Blood Vessel Imaging and Gold Standard Cerebral Digital Subtraction Angiography
Bibliographic record
Abstract
Abstract Background Magnetic resonance intracranial black blood vessel imaging (MR-IBBVI) is a new noninvasive method for evaluating intracranial vessel wall pathology. No previous studies have investigated the efficacy of MR-IBBVI to determine aneurysm size. We aimed to identify the precise diagnosis of MR-IBBVI for the detection and measurement of intracranial aneurysm compared with gold standard cerebral digital subtraction angiography (cDSA). Materials and Methods The retrospective study collected patients of precoiled or postcoiled intracranial aneurysm who were treated at our institute from January 2012 to June 2019 and who had MR-IBBVI, cDSA imaging, and/or three-dimensional time-of-flight sequence of magnetic resonance angiography. The sensitivity and specificity of aneurysm detection by MR-IBBVI and the accuracy of MR-IBBVI for measuring the aneurysm and vessel size were calculated. Results One hundred and twenty patients (61% female) with 132 aneurysms were included into this study. The mean aneurysm size was 5.3 mm (range: 2.2–22.6). Sensitivity and specificity of MR-IBBVI to detect a small aneurysm were 98.74 and 91.21%, respectively. No statistically significant results were observed between MR-IBBVI and DSA for aneurysm detection or any of the evaluated measurement parameters. Conclusion MR-IBBVI is an accurate and highly sensitive method to detect and evaluate the size of an intracranial aneurysm both before and after coiling.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".