Abstract TMP22: Thrombus Volume as Predictor of Successful Recanalization in the Acute Stroke Patients With Thrombolytic Treatment
Bibliographic record
Abstract
Introduction: The prediction of non-recanalization after IV thrombolysis (IVT) may be helpful to avoid unnecessary treatment and determine the strategy of recanalization treatment. Thrombus volume and density can be measured using software, which may provide accurate and reliable information on thrombus characteristics. Hypothesis: We hypothesized that the thrombus volume and/or density could predict non-recanalization after IVT. Methods: This was a post-hoc analysis of prospective cohort who underwent thin-section noncontrast CT (1 or 1.25 mm) and recanalization therapy. Among them, this study considered patients who received IVT due to anterior circulation stroke from Nov. 2006 to Dec. 2015. The volume and density of thrombus were measured semi-automatically using 3-dimensional software. Recanalization was assessed on CT angiography at the end of IVT or conventional angiography in cases further treated with intra-arterial treatment. Successful recanalization was defined as arterial occlusive lesion grade 2 or 3. Results: Among 345 patients considered, 218 with visible thrombus in the intracranial arteries were included for this study. Successful recanalization was achieved in 79 patients (36.2%). Thrombus volume was significantly larger in patients with non-recanalization than those with successful recanalization (median [interquartile range]: 117.8mm 3 [60.0 - 216.8 mm 3 ] vs. 56.9 mm 3 [31.1 - 105.0 mm 3 ]. p<0.001). In the multivariate analysis, thrombus volume was independently associated with non-recanalization (p<0.001). Recanalization failed in all cases with thrombus > 300 mm 3 , and in 41 of 44 cases (93.2%) with thrombus > 200 mm 3 . Thrombus density did not differ between the groups (non-recanalization vs. successful recanalization: 54.0 ± 9.1 vs. 52.6 ± 8.5, p=0.263). Conclusions: Thrombus volume was predictive of non-recanalization after IVT. Measurements of thrombus volume may be helpful to determine the strategy of recanalization treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".