Abstract WP37: Whole Brain CT Perfusion Identifies Ischemic Events in Patients With Mild Neurological Symptoms
Bibliographic record
Abstract
Introduction: More than half of ischemic stroke patients present as minor strokes (NIHSS<6). A lack of thrombolysis guidelines for this population leads to untreated strokes and erroneously treated stroke mimics, producing adverse outcomes. Data suggest that whole brain CT perfusion (WB-CTP) improves detection of ischemia, offering a potential method of ameliorating diagnostic uncertainty in these patients. Hypothesis: WB-CTP can guide clinical decisions by identifying patients with ischemic episodes that would benefit from thrombolysis or early intervention. Methods: This retrospective chart review enrolled 524 consecutive patients receiving WB-CTP with a Toshiba 320 detector scanner between 08/2008 and 06/2015, for acute stroke less than 6 hours from onset and NIHSS<6, and who showed no evidence of intracranial hemorrhage. Patients were excluded for non-diagnostic (n=25) or unreported (n=8) scans and non-ischemic findings (7). For diagnostic accuracy calculations, the reference standard was the final clinical impression suggesting ischemic events, as only 52% had follow-up imaging. Subgroup analyses were performed in patients receiving follow-up imaging. Results: A total of 484 patients (age 17-101, 54% men, mean NIHSS 2.47) were included. Follow-up imaging was performed in 251 patients; 150 underwent MRI with diffusion-weighted imaging (DWI). A summary of diagnostic accuracy values is shown in table below. WB-CTP is highly specific with a high positive predictive value in all groups and has moderate to high negative predictive value. Positive and negative likelihood ratios were 21.24 and 0.5 in the whole group analysis. Conclusions: Positive WB-CTP findings may warrant early intervention, including thrombolysis, while negative findings alone are not a sufficient basis upon which to confidently withhold interventions.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".