HUBUNGAN KADAR TROMBOSIT DENGAN ALBERTA STROKE PROGRAM EARLY CT SCORE (ASPECTS) PADA STROKE ISKEMIK AKUT: CORRELATION BETWEEN PLATELET COUNT AND ALBERTA STROKE PROGRAM EARLY CT SCORE (ASPECTS) IN ACUTE ISCHEMIC STROKE
Bibliographic record
Abstract
Latar belakang : Peningkatan kadar trombosit pada stroke iskemik akut mempengaruhi faktor koagulopati darah sehingga berdampak pada luasnya daerah iskemik. Tujuan : Penelitian ini bertujuan untuk mengetahui hubungan kadar trombosit dengan Alberta Stroke Program Early CT Score ( ASPECTS ) pada stroke iskemik akut. Metode : Penelitian ini bersifat deskriptif analitik dengan uji potong lintang. Pengambilan sampel memakai data sekunder di bagian neurologi RSUP Prof. Dr. R.D. Kandou, Manado periode Oktober -Desember 2017. Analisa data statistik memakai SPSS 22. Hasil : Hasil pada penelitian ini menunjukkan bahwa hasil uji Fisher Exact diperoleh tidak ada hubungan bermakna antara trombosit dengan ASPECTS ( p = 0,553 ). Faktor – faktor resiko lainnya seperti tekanan darah, kadar Low Density Lipoprotein (LDL), Gula Darah Sewaktu (GDS) dan kolesterol juga tidak didapatkan hubungan yang bermakna dengan nilai ASPECTS dengan nilai p > 0,05. Kesimpulan : Tidak terdapat hubungan yang signifikan antara kadar trombosit dengan Alberta Stroke Program Early CT Score (ASPECTS) pada stroke iskemik akut di RSUP Prof.Dr.R.D.Kandou, Manado. Kata kunci : ASPECTS, stroke iskemik akut, trombosit. ABSTRACT Introduction : The elevation of platelet count in acute ischemic stroke influencing the blood coagulation factors due to broadening ischemic area. Aim : This study aim to find the correlation of platelet count with Alberta Stroke Program Early CT Score (ASPECTS) in acute ischemic stroke. Methods : A descriptive – analysis cross sectional study was conducted in the Neurological Department of National Hospital Prof. R. D.Kandou Manado from October until December 2017 by using secondary data. Result : Based on Fisher Exact test, there is no correlation between Platelet count and ASPECTS ( p = 0,553 ), as well as other influence factor such as blood pressure, Low Density Lipoprotein (LDL), random blood sugar and cholesterol level ( p > 0,05 ). Discussion : This study found a no significant relationship between platelet count and ASPECTS of ischemic stroke in acute phase in RSUP.Prof.Dr.R.D.Kandou, Manado. Keywords: ASPECTS, acute ischemic stroke, platelet.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".