Wert des CBV-ASPECTS im Vergleich zum CTA-ASPECTS bei Patienten mit akutem ischämischem Schlaganfall
Bibliographic record
Abstract
Objectives CTP images analyzed with the Alberta stroke program early CT scale (ASPECTS) have been shown to be optimal predictors of clinical outcome. In this study we compared two biomarkers, the cerebral blood volume (CBV)- ASPECTS and the CTA-ASPECTS as predictors of clinical outcome after thrombectomy. Methods Stroke patients with thrombosis of the M1 segment of the middle cerebral artery were included in our study. All patients underwent initial multimodal CT with CTP and CTA on a modern CT scanner. Treatment consisted of full dose intravenous tissue plasminogen activator, when applicable, and mechanical thrombectomy. Three neuroradiologists separately scored CTP and CTA images with the ASPECTS score. Results Sixty-five patients were included. Median baseline CBV-ASPECTS and CTA-ASPECTS for patients with favourable clinical outcome at follow-up were 8 [interquartile range (IQR) 8-9 and 7-9 respectively]. Patients with poor clinical outcome showed a median baseline CBV-ASPECTS of 6 (IQR 5-8, P< 0.0001) and a median baseline CTA-ASPECTS of 7 (IQR 7-8, P = 0.18). Using CBV-ASPECTS and CTAASPECTS raters predicted futile reperfusions in 96 % and 56 % of the cases, respectively. Conclusions CBV-ASPECTS is a significant predictor of clinical outcome in patients with acute ischemic stroke treated with 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 | 0.004 |
| 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) | 0.001 | 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".