Effect of Old Infarction on Alberta Stroke Program Early CT Score for Acute Stroke Diagnosis
Bibliographic record
Abstract
This study aimed to analyze the effect of CT images including old infarction on the Alberta stroke program early CT score (ASPECTS) for acute stroke diagnosis. Location of acute and old infarction through automatically calculated ASPECTS and diffusion weighted image interpretation of patients who have undergone both CT scan and diffusion weighted image MRI for acute stroke diagnosis among patients with suspected cerebral infarction who visited the emergency room After comparison, it was determined whether the location of old infarction had an effect on ASPECTS. The Pearson chi-square test was analyzed for the ASPECT score according to the presence or absence of cerebral infarction, and SPSS version 28.0 was used as the analysis program, and it was judged to be significant when the P value was less than 0.05. As a result of this study, in the analysis of whether old infarction affects ASPECTS, chronic cerebral infarction showed an effect on ASPECTS in the group divided into 10, 9, 8, and 7 points or less, and the group divided into 10 and 9 points. However, in the analysis of the groups with scores of 9, 8, and 7 or less, excluding 10, and the group divided into 8 or more and 7 or less, old infarction did not show any effect on ASPECTS. In conclusion, it is believed that complementary research is needed to diagnose acute stroke and predict prognosis in patients with various brain diseases using an improved artificial intelligence program through large-scale research in ASPECTS.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".