Sistem Skoring Alberta Stroke Program Early CT Score untuk Evaluasi Kasus Stroke Iskemik
Bibliographic record
Abstract
Sistem skoring Alberta Stroke Program Early CT Score (ASPECTS) merupakan alat skoring semi – kuantitatif sederhana untuk mengevaluasi gambaran iskemi akut pada CT scan non kontras atau MRI. Pada awal publikasinya, sistem skoring ini dianggap dapat memprediksi outcome fungsional dan kejadian transformasi perdarahan pada pasien yang menjalani trombolisis intravena dengan alteplase. Namun rekomendasi terbaru tidak lagi merekomendasikannya. Data efektivitas trombektomi mekanik pada populasi dengan nilai ASPECTS ≤ 5 belum cukup. Tulisan ini membahas cara menilai, kegunaan serta implikasi sistem skoring ASPECTS terhadap tatalaksana pasien dengan stroke iskemi akut Alberta Stroke Program Early CT Score (ASPECTS) is a simple semi-quantitative scoring system to evaluate the noncontrast CT Scan or MRI imaging of acute ischemic lesion. Originally, the scoring system was considered able to predict the functional outcome and hemorrhagic transformation in patient undergoing intravenous thrombolysis with alteplase. However, the latest guideline does not recommend ASPECTS to determine the eligibility of patient undergoing alteplase therapy. Data regarding the efficacy of MT in patient with ASPECTS ≤ 5 is scarce and is still a subject of debate. This article will discuss the evaluation and the implication of ASPECTS scoring system in the management of acute ischemic stroke.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".