Perfusion CT Imaging Follows Clinical Severity in Left Hemispheric Strokes
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to assess how imaging findings on admission perfusion CT (PCT) and follow-up noncontrast CT (NCT), and their changes over time, correlate with clinical scores of stroke severity measured on admission, at discharge and at 6-month follow-up. METHODS: Fifty-two patients with suspected hemispheric acute ischemic stroke underwent a PCT within the first 24 h of symptom onset and a follow-up NCT of the brain between 24 h and 3 months after the initial stroke CT study. NIH Stroke Scale (NIHSS) scores were recorded for each patient at admission, discharge and 6 months; modified Rankin scores were determined at discharge and 6 months. Baseline PCT and follow-up NCT were analyzed quantitatively (volume of ischemic/infarcted tissue) and semiquantitatively (anatomical grading score derived from the Alberta Stroke Program Early CT Score). The correlation between imaging volumes/scores and clinical scores was assessed. Analysis was performed for all patients considered together and separately for those with right and left hemispheric strokes. RESULTS: Significant correlations were found between clinical scores and both quantitative and semiquantitative imaging. The volume of the acute PCT mean transit time lesion showed best correlation with admission NIHSS scores (R2 = 0.61, p < 0.001). This association was significantly better for left hemispheric strokes (R(2) = 0.80, p < 0.001) than for right hemispheric strokes (R2 = 0.39, p = 0.131). Correlation between imaging and NIHSS scores was better than correlation between imaging and modified Rankin scores (p = 0.047). The correlation with discharge clinical scores was better than that with 6-month clinical scores (p = 0.012). CONCLUSIONS: Baseline PCT and follow-up NCT volumes predict stroke severity at baseline, discharge and, to a lesser extent, 6 months. The correlation is stronger for left-sided infarctions. This finding supports the use of PCT as a surrogate stroke outcome measure.
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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 | 0.003 |
| 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) | 0.002 | 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".