Çocuklarda Antiepileptik İlaçlar
Bibliographic record
Abstract
Antiepileptik ilaçlar (AEİ) ile epilepsi tanılı çocukların yaklaşık %70’nde nöbetler kontrol altına alınır. Seçilecek ilaç hastaların yaşı, nöbet semiyolojisi, epileptik sendrom, farmakogenetik yaklaşım ile belirlenir. Tedavide amaç bir veya daha fazla antiepileptik ilacın etkili dozunu muhafaza ederek nöbet oluşumunu önlemektir. Son yıllarda yan etki profili ve ilaç etkileşimi daha az olan birçok yeni nesil AEİ gündeme gelmiş olup bir kısmı klinik pratikte kullanılmaktadır. Çocuklarda, erişkinlere göre AEİ’lerin klinik kullanımları ve yan etkileri farklıdır. AEİ’lerin çocuk yaş grubuna ait özelliklerinin bilinmesi tedavi başarısında son derece önemlidir. Derlememizde çok sayıda güncel kaynak incelenerek çocukluklarda sık kullanılan AEİ’lerin etki mekanizmaları, farmakokinetik özellikleri, klinik kullanım ve dozları, yan etki ve ilaç etkileşimleri ele alınmıştır.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".