RELIABILITY OF PERSIAN EARLY COMPUTED TOMOGRAPHY SCORE IN PATIENTS WITH BRAIN INFARCTION
Bibliographic record
Abstract
Background: The one-third middle cerebral artery (1/3 MCA) method and Alberta Stroke Program Early Computed Tomography Score (ASPECTS) were used to detect significant early ischemic changes on brain computed tomography (CT) of patients with acute stroke. We designed the Persian Early CT Score (PECTS) and compared it with the above systems. Methods: The tomograms were chosen from the stroke data bank of Ghaem Hospital, Mashhad, in 2008. The inclusion criteria were the presence of MCA territory infarction and performance of CT within 6 hours after stroke onset. Axial CTs were performed on a third-generation CT scanner (Siemens, ARTX, Germany). Section thickness above posterior fossa was 10 mm (130 kV, 150 mAs). Films were made at window level 35 HU. The brain CTs were scored by four independent radiologists based on the ASPECTS, 1/3 MCA method, and PECTS. The readers were blinded to the clinical information except the symptom side. Cochrane Q and Kappa tests were used for statistical analysis. Results: Twenty four CT scans with sufficient quality were available. The difference in distribution of dichotomized ≤7 and >7 ASPECT scores between the four raters was significant; Q=13.071, df=3, P=0.04. The difference in distribution of dichotomized >1/3 and ≤1/3 MCA territory involvement between 4 raters was also significant; Q=13.5, df=3, P=0.004. Distribution of dichotomized <6 and ≥6 scores based on PECTS system between the four raters was not different; Q=6.349, df=3, P=0.096.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".