Early Ischemic Stroke Assessment with ASPECTS: A Case Report Highlighting the Radiologist's Role in a Limited-Resource Setting
Bibliographic record
Abstract
Background: Ischemic stroke is a leading cause of morbidity and mortality globally, particularly in resource-limited settings. Non-contrast computed tomography (NCCT) is often the primary imaging modality available in these settings, and the Alberta Stroke Program Early CT Score (ASPECTS) is a crucial tool for assessing early ischemic changes in NCCT. This case report highlights the importance of ASPECTS in guiding clinical decisions and prognostication in a resource-limited setting. Case presentation: A 79-year-old male presented to the emergency unit at Negara General Hospital, a rural facility in Bali, with acute onset of right-sided hemiparesis and speech difficulty. NCCT showed a hypodense lesion with ill-defined margins in the left insular cortex, left caudate nucleus, left internal capsule, and left frontotemporoparietal lobes, consistent with a subacute cerebral infarction in the middle cerebral artery (MCA) territory, with an ASPECTS score of 2. Due to the extensive ischemic burden and the limited availability of advanced treatment options, conservative management was chosen. The radiologist's interpretation of the ASPECTS score played a critical role in guiding the clinical team's decision-making and informing the patient's family about the prognosis. Conclusion: ASPECTS is an essential tool for predicting stroke outcomes, with lower scores correlating with larger infarct volumes and poorer prognoses. In resource-limited settings, radiologists play a vital role in interpreting ASPECTS scores to guide clinical management and provide accurate prognostic information to patients and their families.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".