Efficacy of thrombus density on noninvasive computed tomography neuroimaging for predicting thrombus pathology and patient outcome after mechanical thrombectomy in acute ischemic stroke
Bibliographic record
Abstract
Background and Purpose: The aim of this study was to investigate the efficacy of thrombus density on noninvasive computed tomography (CT) neuroimaging for predicting thrombus pathology and patient outcome after mechanical thrombectomy in acute ischemic stroke. Materials and Methods: This retrospective chart and imaging review included patients that were treated by mechanical thrombectomy at Siriraj Hospital according to the American Heart Association/American Stroke Association guidelines for the early management of patients with acute ischemic stroke from March 2010 to February 2015 study period. Preintervention noncontrast CT (NCCT), CT angiography (CTA), and/or contrast-enhanced CT (CECT) images were interpreted using CT densitometry. Pathology results were classified as white, red, or mixed thrombi. The result of treatment was evaluated by the modified Rankin Scale at 90 days after treatment. Results: From 97 included patients – 97 NCCT images, 48 CTA images, 48 CECT images, and 54 pathologic results of cerebral thrombi were included in the final analysis. Mean clot Hounsfield unit values on NCCT, CTA, and CECT were significantly different between red and white thrombus (P = 0.001 on NCCT, P = 0.03 on CTA, and P = 0.001 on CECT), and between red and mixed thrombus (P = 0.043 on NCCT and P = 0.002 on CTA). However, no significant difference was observed between white thrombus and mixed thrombus (P = 0.09 on NCCT, P = 1.00 on CTA, and P = 0.054 on CECT). There was no significant correlation between type of cerebral thrombus or clot density and the result of treatment. Conclusion: Thrombus density on CT was found to be a significant predictor of thrombus pathology; however, no significant association was observed between thrombus type or clot density and patient outcome after mechanical thrombectomy.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".