Diagnostic accuracy of whole-brain computed tomography perfusion for detection of ischemic stroke in patients with mild neurological symptoms
Bibliographic record
Abstract
Mild and minor acute neurological symptoms may lead to diagnostic uncertainty, resulting in a heterogeneous group of patients with true ischemic events and stroke mimics with a potential for poor outcomes. More than half of ischemic stroke patients present as minor strokes (National Institutes of Health Stroke Scale score <6). Whole-brain computed tomography perfusion can be used as a diagnostic test for minor stroke, offering a potential method of reducing diagnostic uncertainty in these patients. We hypothesize that whole-brain computed tomography perfusion imaging features could accurately predict infarction in patients with minor neurological deficits. This retrospective chart review enrolled consecutive patients suspected of acute ischemic stroke with a National Institutes of Health Stroke Scale score <6, who underwent whole-brain computed tomography perfusion and follow-up diffusion-weighted magnetic resonance imaging at our institution. Sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios were calculated for whole-brain computed tomography perfusion, using follow-up diffusion-weighted magnetic resonance imaging as a reference standard. A total of 524 patients (mean age: 67 years; range: 17-96 years; 56% men) met the inclusion criteria. Patients were excluded for non-diagnostic ( n = 25) or missing maps ( n = 8) scans, non-ischemic findings ( n = 7), and lack of follow-up magnetic resonance imaging ( n = 336). The final analysis included 148 patients who underwent diffusion-weighted magnetic resonance imaging. Whole-brain computed tomography perfusion has a sensitivity of 0.57 (95% CI: 0.45-0.69) and a specificity of 0.82 (95% CI: 0.71-0.90). The positive and negative predictive values and positive and negative likelihood ratios were 75%, 67%, 3.09, and 0.53, respectively. Our analysis suggests that although whole-brain computed tomography perfusion may offer some value as an adjunctive test for improving confidence in offering stroke treatment, it is not sufficiently sensitive or specific to accurately predict cerebral infarcts in patients with minor neurological symptoms.
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 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 | 0.002 | 0.017 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".