Correlation of neurological clinical and brain computed tomography findings of stroke patients – a multicenter study from a low-income setting
Bibliographic record
Abstract
Abstract Background: In developing countries like Uganda, the burden of stroke is growing and causing significant morbidity and disability with high mortality rates. Neuroimaging is required to differentiate ischemic stroke from an intracerebral hemorrhage, as well as to diagnose entities other than stroke. Therefore, understanding the clinico-demographic features and brain Computed Tomography (CT) findings of stroke as well as their correlation is of utmost value in stroke management. Hence, this study sought to determine the clinical and brain CT scan findings of stroke patients attending selected centers in Kampala. Mention ASPECTS briefly Methods: This was a cross-sectional study of clinically suspected stroke patients at three selected hospitals in Kampala, Uganda. All brain CT scans of patients with suspected stroke were evaluated and The Alberta stroke programme early CT score (ASPECTS) a 10-point quantitative topographic CT scan score used for middle cerebral artery (MCA) stroke patients. Data on the clinical-demographic and cranial CT findings were collected, and entered into REDCap software. Univariate analysis was used to describe the clinico-demographic and cranial CT features of stroke and summarized them as percentages. Further analysis was done at bivariate and multivariate levels to determine the adjusted odds ratios as a measure of association with a 95% confidence interval. Results: Of the 270 study participants,141(52.2%) were male. 162(60%) had CT findings of stroke, and 90(33.3%) had normal cranial CT findings. While 18(6.7%) had other CT findings like a tumor, dural hemorrhage, epidermoid cyst, and others. The 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-value =0.021]. Conclusion: Currently, non-contrasted brain CT is the first line imaging modality for diagnosis, differentiation of the stroke types, patient management, treatment, and timely referral to a stroke center. The clinical diagnosis of stroke is inadequate to exclude other stroke mimics. Hypertension and advanced age are the most prevailing risk factors attributed to both ischemic and hemorrhagic stroke and patients over 65 years were associated with a worse ASPECT score.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".