Computed Tomography Workup of Patients Suspected of Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To determine whether perfusion computed tomography (PCT) adds value to noncontrast head CT (NCT), CT angiogram (CTA), and clinical assessment in patients suspected of acute ischemic stroke. METHODS: We retrospectively reviewed 165 patients with acute ischemic stroke. PCT was used to calculate the volumes of infarct core and ischemic penumbra on admission. Other imaging data included Alberta Score Program Early CT Score, site of occlusion, and collateral flow. Clinical data included age, time, National Institutes of Health Stroke Scale at baseline, treatment type, and modified Rankin score (mRS) at 90 days. Recanalization status was assessed on follow-up imaging. In a first multivariate regression analysis, we assessed whether volumes of PCT penumbra and infarct core could be predicted from clinical variables, NCT, or CTA, or whether they represented independent information. In a second multivariate regression analysis, we used mRS at 90 days as outcome and determined which variables predicted it best. RESULTS: Of 165 patients identified, 76 had a mRS score of 0 to 2 at 90 days, 89 had a mRS score >2. PCT infarct could be predicted by clinical data, NCT, CTA, and combinations of this data (P<0.05). PCT penumbra could not be predicted by clinical data, NCT, and CTA. All of the variables but NCT and CTA were significantly associated with 90-day mRS outcome. The single most important predictor was recanalization status (P<0.001). PCT penumbra volume (P=0.001) was also a predictor of clinical outcome, especially when considered in conjunction with recanalization through an interaction term (P<0.001). CONCLUSIONS: PCT penumbra represents independent information, which cannot be predicted by clinical, NCT, and CTA data. PCT penumbra is an important determinant of clinical outcome and adds relevant clinical information compared with a stroke CT workup, including NCT and CTA.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 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".