DOI 10.1007/s00330-007-0696-9 NEURO
Bibliographic record
Abstract
Perfusion CT in acute stroke: prediction of vessel recanalization and clinical outcome in intravenous thrombolytic therapy Abstract This study evaluated per-fusion computed tomography (PCT) for the prediction of vessel recanali-zation and clinical outcome in patients undergoing intravenous thrombolysis. Thirty-nine patients with acute ische-mic stroke of the middle cerebral artery territory underwent intravenous thrombolysis within 3h of symptom onset. They all had non-enhanced CT (NECT), PCT, and CT angiography (CTA) before treatment. The Alberta Stroke Program Early Computed Tomography (ASPECT) score was applied to NECT and PCT maps to assess the extent of ischemia. CTA was assessed for the site of vessel occlusion. The National Institute of Health Stroke Scale (NIHSS) score was used for initial clinical assess-ment. Three-month clinical outcome was assessed using the modified Rankin scale. Vessel recanalization was determined by follow-up ultra-sound. Of the PCT maps, a cerebral blood volume (CBV) ASPECT score of>6 versus ≤6 was the best predictor for clinical outcome (odds ratio, 31.43; 95 % confidence interval, 3.41–289.58; P<0.002), and was superior to NIHSS, NECT and CTA. No significant differences in ASPECT scores were found for the prediction of vessel recanalization. ASPECT score applied to PCT maps in acute stroke patients predicts the clinical outcome of intravenous thrombolysis and is superior to both early NECT and clinical parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.013 | 0.001 |
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 teacher head, 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".