Correlation between clinical and brain computed tomography findings of stroke patients: A cross‐sectional study
Bibliographic record
Abstract
Abstract Background and Aims In developing countries, the burden of stroke is growing and causing significant morbidity and disability with high mortality rates. Neuroimaging plays a crucial role in differentiating ischemic stroke from an intracerebral hemorrhage, as well as entities other than stroke. This study sought to determine the correlation between the clinical and brain CT scan findings of stroke patients attending three hospitals in Kampala, Uganda. Methods This was a cross‐sectional study of clinically suspected stroke patients who were sent for brain CT scan at three selected hospitals in Kampala, Uganda. All brain CT scans of patients with suspected stroke were evaluated and the Alberta stroke program early CT score (ASPECTS) was used for middle cerebral artery (MCA) strokes. Univariate analysis was used to describe the clinico‐demographic and brain CT features of stroke and summarized them as percentages. Bivariate and multivariate analysis were used to determine the adjusted odds ratios as a measure of association with a 95% confidence interval (CI). Results Of the 270 study participants, 141 (52.2%) were male. 162 (60%) had CT findings of stroke, and 90 (33.3%) had normal brain CT findings. Eighteen (6.7%) had other CT findings like tumor, dural hemorrhage, epidermoid cyst, and others. Ischemic stroke, hemorrhagic stroke, and subarachnoid hemorrhage accounted for 124 (45.9%), 34 (12.6%), and 4 (1.5%) respectively. Limb weakness (55.2%), headache (41.1%), and loss of consciousness (39.3%) were associated with stroke findings on CT. Among the acute ischemic strokes, 30 (73.2%) had a worse (0–7) ASPECT score. Those aged ≥65 years were associated with a worse ASPECTS [AOR: 22.01, (95% CI: 1.58–306.09) p = 0.021]. Conclusion More than a third of patients with a clinical diagnosis of stroke had either no CT features of stroke or had other findings. The most commonly affected vascular territory was left MCA. Old age was strongly associated with having the worst ASPECTS score.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".