Palyatif Bakımda Hasta Değerlendirmesi ve Skalalar
Bibliographic record
Abstract
Palyatif bakimda ayrintili ve iyi yapilmis bir degerlendirme kanser hastalarinin genel iyilik halinin degerlendirilmesi icin cok onemlidir. Degerlendirmede detayli bilgi toplayip hastaya verilecek palyatif bakimi yonlendirmek temel amactir. Bu amacla hastalarin mevcut durumlarinin ortaya konmasi, ihtiyaclarinin belirlenmesi ve buna gore bir oncelik sirasi olusturularak multidisipliner bir yaklasim izlenmesi gerekmektedir. Hastanin ilk degerlendirmesi hastayi takip eden hekim tarafindan yapilmali ve palyatif bakim ihtiyaci olup olmadigini anlamak amaciyla tarama seklinde olmalidir. Tarama kriterlerini karsilayan hastalara ayrintili bir palyatif bakim degerlendirmesi yapilmalidir. Bu hastalarin belirti ve klinik bulgulari cok kisa surede degisebileceginden hastalarin her muayenesinde palyatif bakim ihtiyaci icin tarama yapilmalidir. Yapilan degerlendirmeler not edilmeli, ekibin diger uyeleri tarafindan anlasilabilir ve kolay erisilebilir olmalidir. Hastaligin iyi yonetilebilmesi icin hastanin semptomlarinin, karsilastigi yan etkilerin, fonksiyonelliginin, yasam kalitesi ve tedaviye uyumunun cok dikkatli degerlendirilmesi gerekir. Palyatif bakim hastalarinin degerlendirilmesinde pek cok olcek kullanilmaktadir. En sik kullanilan olcekler ornegin Edmonton Semptom Tanilama Olcegi, Karnofsky Performans Skalasi, Katz’in Gunluk Yasam Aktiviteleri Indeksi makalede tartisilacaktir.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".