P.062 Does the intensity of brain parenchymal contrast staining on post-recanalization dual energy head CT (DECT) of stroke patients predict the fate of brain tissue?
Bibliographic record
Abstract
Background: On DECT, the ratio of maximum iodine concentration within parenchyma compared to the superior sagittal sinus has been shown to predict hemorrhagic transformation. We aimed to determine if this ratio also predicts the development of an infarct. Methods: 53 patients with small infarct cores (ASPECTS≥7) and good endovascular recanalization (mTICI 2b/3) were enrolled. Maximum brain parenchymal iodine concentration as per DECT relative to the superior sagittal sinus (iodine ratio) was correlated with the development of an infarct on follow up CT. Results: All patients showed contrast staining, 52 developed infarcts in the area of staining. The extent of infarction (smaller, equal or larger than area of staining) did not correlate with the iodine ratio. Conclusions: Brain parenchyma with contrast staining on post-procedure head CT almost invariably goes on to infarct, however the extent of infarct development is not predicted by the intensity of contrast staining. n=53 patients with successful recanalization of anterior circulation LVO infarct (TICI2b,3) with post procedural parenchymal iodine staining There was no correlation between the degree of contrast staining on initial post procedural CT as expressed in iodine ratio and F/U infarct extent.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".