Evaluation of Traumatic Subarachnoid Hemorrhage Using Susceptibility-Weighted Imaging
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: SWI is an MR imaging technique that is very sensitive to hemorrhage. Our goal was to compare SWI and CT to determine if SWI can show traumatic SAH in different parts of the subarachnoid space. MATERIALS AND METHODS: Twenty acute TBI patients identified by CT with SAH underwent MR imaging scans. Two neuroradiologists analyzed the CT and SWI data to decide whether there were SAHs in 8 anatomical parts of the subarachnoid space. RESULTS: Fifty-five areas with SAH were identified by both CT and SWI. Ten areas were identified by CT only and 13 by SWI only. SAH was recognized on SWI by its very dark signal intensity surrounded by CSF signal intensity in the sulci or cisterns. Compared with the smooth-looking veins, SAH tended to have a rough boundary and inhomogeneous signal intensity. In many instances, blood in the sulcus left an area of signal intensity loss that had a "triangle" shape. SWI showed 5 more cases of intraventricular hemorrhage than did CT. CONCLUSIONS: SAH can be recognized by SWI through its signal intensity and unique morphology. SWI can provide complementary information to CT in terms of small amounts of SAH and hemorrhage inside the ventricles.
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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 | 0.000 | 0.003 |
| 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".