MAJOR RISK FACTORS, CLINICAL AND LABORATORY CHARACTERISTICS OF PATIENTS WITH HEPATOCELLULAR CARCINOMA; A RETROSPECTIVE STUDY AT TIKUR ANBASSA HOSPITAL, ADDIS ABABA UNIVERSITY, ADDIS ABABA, ETHIOPIA.
Bibliographic record
Abstract
BACKGROUND: Hepatocellular carcinoma (HCC) is a major cause of cancer death worldwide accounting for over half a million deaths per year. Hepatocellular carcinoma can occur secondary to viral hepatitis, HBV or HCV. It can also occur secondary to other causes of Cirrhosis (alcoholism being the other most common cause). OBJECTIVES: To describe clinical and laboratory characteristics of Hepatocellular carcinoma in a tertiary care hospital in Addis Ababa, Ethiopia. METHODS: A retrospective study was conducted in patients admitted to Tikur Anbassa specialized Hospital with a diagnosis of Hepatocellular carcinoma during the period of January 1, 2013 to Dec. 31, 2015. Data were collected using structured questionnaire on basic demographic factors, behavioral risks, laboratory profiles and imaging reports. Descriptive analysis was performed on the data collected. RESULTS: Fifty one patients fulfilled the criteria for Hepatocellular carcinoma in the study period. Thirty nine were males and 12 were females. Hepatitis B and C viruses were found to be the causes for HCC in 48% of the cases. History of alcohol abuse was documented in 45% % of the individuals. About 26% of the patients had Ascites, 35% were found to have portal vein thrombosis, The child-Pugh score for patients who had complete profile were Child A 46%, Child B an equal percentage of 46% andfor Child C 0.7%. CONCLUSION: The contribution of Hepatitis virus is high with equivalent proportion of HBV and HCV. Alcohol intake and unidentified risk factors have also played for another half of the causes. Almost a third of patients have Portal vein thrombosis and 96% were either Child Pugh A or B. Enhancing immunization coverage frequent use of infection prevention and availability of treatment for viral hepatitis will help to reduce Hepatocellular carcinoma.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".