Correlation between acute ischemic stroke onset with Alberta stroke program early CT score
Bibliographic record
Abstract
Background: Alberta Stroke Program Early CT Score (ASPECTS) is a valid method for assessing early ischemic changes in the middle cerebral artery from a CT scan of patient with acute ischemic stroke. One of the factors that influence ASPECTS is stroke onset time, where a very subtle level of hypodensity in early onset can provide poor reliability on ASPECTS assessments. Aim of the study was to determine the relationship between the onset of acute ischemic stroke and ASPECTS.Methods: This study used a cross-sectional design with Chi-Square method in patients with acute ischemic stroke and anterior circulation stroke treated in The Stroke Corner and Integrated Ward of Haji Adam Malik General Hospital during the months of February - May 2019. All patients were evaluated for ASPECTS and stroke onset at admission. Stroke onset was divided into 3 parts: Under 24 hours, 24 - <48 hours and 48-72 hours. ASPECTS value was assessed by 2 observers. Authors categorized the ASPECT value into 2 groups: Low (≤7) and High (˃7).Results: Among 36 patients with Acute Ischemic Stroke, mean age was 55.7±13.9 years old, which male and female shares equal number by 18 persons (50%). Mean ASPECTS score was 7.2±2.0. This research found 5 patients (13.9%) with less than 24 hours onset and low ASPECTS score, 3 patients (8.3%) with 24 - <48 hours onset and low ASPECTS score, 7 patients (19.4%) with 24 - <48 hours onset and high ASPECTS score, 8 patients (22.2%) with 48-72 hours onset and low ASPECTS score, and 2 patients (5.6%) with 48-72 hours of onset and high ASPECTS score. Valuation of ASPECTS from both observers was considered as excellent (statistic K value = 0.9).Conclusions: ASPECTS has a significant relationship with stroke onset (p=0.029) and the initial ischemic change will be seen more clearly with increasing stroke onset time.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".