P.056 Dual-energy CT for differentiating intracerebral hemorrhage from Contrast Extravasation after Acute Ischemic Stroke Intervention (DECT-ICH)
Bibliographic record
Abstract
Background: Thrombolysis (tPA) and endovascular thrombectomy (EVT) are interventions for acute ischemic stroke (AIS) that can be accompanied by intracerebral hemorrhage (ICH), which can alter the patient’s management, or contrast extravasation (CE), which is relatively benign. Previous retrospective studies have shown that dual-energy CT (DECT) is significantly more accurate for differentiating ICH from CE compared to conventional, single-energy CT (SECT). We are performing a prospective study to investigate this question. Methods: Our primary outcome is the sensitivity and specificity of DECT in differentiating ICH from CE. In AIS patients who receive intervention, we will be performing a DECT scan at the same time as the standard-of-care SECT scan at 24 hours post-intervention. In patients who have a hyperdensity on CT, a repeat scan will be done at 72-hours, which will be used as the gold-standard to determine if the hyperdensity was ICH or CE. Results: We expect that DECT will be significantly more sensitive and specific for differentiating ICH from CE compared to SECT. Conclusions: This study will determine if DECT is superior to SECT in differentiating ICH from CE, validate the use of DECT in AIS patients who receive intervention, and potentially change the imaging paradigm for acute stroke in the future.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".