Hepatocellular carcinoma after kidney transplantation: analysis of Hong Kong Renal Registry
Bibliographic record
Abstract
Kidney transplant recipients have increased risk of cancers when compared with the general population. Hepatocellular carcinoma (HCC) is extremely important in Asia where hepatitis B virus (HBV) infection is endemic. The aim is to study the epidemiological and clinical aspects of all de novo HCC in our kidney transplant recipients. Moreover, various preventive strategies which may help to optimize the outcome will also be discussed. A retrospective review of all patients who developed HCC after kidney transplantation between May 1972 and December 2011 in Hong Kong, based on the data from Hong Kong Renal Registry. After a follow-up period of 40,246 person-years, 20 patients (males 15: females 5) developed HCC. The annual incidence was 49.7/100,000 persons per year. Among them, 16 were HBV carriers, 2 were hepatitis C (HCV) carriers and 2 had HBV and HCV co-infection. Presence of HBV infection was associated with 78-fold higher risk for HCC development. Majority (85%) were asymptomatic when HCC was diagnosed by ultrasound or alpha-fetoprotein surveillance. All patients diagnosed by surveillance received active treatment while 2/3 of symptomatic patients could only receive symptomatic care and died rapidly. In conclusion, HBV infection is the major etiological factor for HCC development in kidney transplant recipients in HBV endemic areas. Regular HCC surveillance appeared to be able to detect early stage cancers which are amenable to treatment and offer the best hope of cure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".