Beyond conventional imaging: A systematic review and meta-analysis assessing the impact of computed tomography perfusion on ischemic stroke outcomes in the late window
Bibliographic record
Abstract
Background: Non-contrast cranial computed tomography (NCCT) and CT angiogram (CTA) have become essential for endovascular treatment (EVT) in acute stroke. Patient selection may improve when CT perfusion (CTP) imaging is also added for patient selection. We aimed to analyze the effects of implementing CTP in acute ischemic stroke (AIS) patients’ treatment to assess whether stroke outcomes differ in the late window. Methods: We searched the PubMed, Embase, and Web of Sciences databases to obtain articles related to CTA and CTP in EVT. Collected patient data were split into two groups: the CTP and control (NCCT + CTA) cohorts. Primary outcomes evaluated were modified Rankin Scale (mRS) scores, symptomatic intracranial hemorrhages (sICHs), mortality, and successful recanalization. Results: There were 14 studies with 5809 total patients in the final analysis: 2602 received CTP and 3202 were in the control group. CTP/CTA patients showed significantly lower rates of 90-day stroke-related mortality (odds ratio (OR) = 0.72, 95% confidence interval (CI) = 0.60–0.87, p < 0.01) and significantly higher successful recanalization (OR = 1.42, 95% CI = 1.06–1.94, p < 0.01) compared with CTA-only patients. Analysis of other outcomes including functional independence (mRS = 0–2), critical times, and intracranial hemorrhages was non-significant ( p > 0.05). Conclusion: The study highlights the usefulness of CTP-guided therapy as a supplementary tool in EVT selection in the late window. Although the addition of CTP resulted in lower mortality, the favorable outcomes did not improve. Further evidence is required to establish a clearer understanding of the potential advantages or limitations of incorporating CTP in stroke imaging.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".