ANTI HCV DAN JUMLAH PENDERITA JANGKITAN (PREVALENSI INFEKSI) VIRUS HEPATITIS C
Bibliographic record
Abstract
ABSTRACTIn various prevalence Hepatitis C Virus (HCV) infections was found all over the world. There is no accurate data about the spreading of HCV infection in Indonesia. Indonesian Department of Health makes a program to make a database from all provinces in Indonesiaabout prevalence of HCV. This is supported by request from the district Health Department of Daerah Istimewa Yogyakarta (DIY)Province to conduct HCV infection surveillance in 2007. Data of HCV infection from blood transfusion service show approximately 2%.To complete the basic data by knowing the prevalence of HCV in Dr. Sardjito General Hospital Yogyakarta at DIY province. This is an Hospital Yogyakarta at DIY province. This is anHospital Yogyakarta at DIY province. This is an observational descriptive study about HCV infection prevalence in Dr. Sardjito General Hospital Yogyakarta. Data was collected fromSeptember 2007−August 2008 conducted on Microbiology, Immunology & Infection Sub Laboratory of Clinical Pathology Departmentin Dr Sardjito General Hospital Yogyakarta. There was 753 request of anti HCV test from September 2007−August 2008. Initial reactiveresult was found in 131 patients. In the first quarter in 2007 (January−April 2007) there were 27 initial reactive from 175 samples ofanti HCV test (15.42%). In the second quarter were found 36 initial reactive out of 176 samples of anti HCV test (20.45%) and in thelast trimester in 2007 were found 19 initial reactive out of 134 samples (14.17%). In 2008, first quarter were found 21 positive resultout of 123 samples (17.03%) and in the second quarter (May−August 2008) were found 32 positive result out of 145 samples (22.06%).There are gradually increased percentage rates of initial reactive anti HCV test based on the result from each trimester HCV test based on the result from each trimesterHCV test based on the result from each trimester (15.42−20%, 45−14 %, 17−17%, 03−22.06%)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".