Anonymous pilot study of hepatitis C virus prevalence in liver transplant surgeons
Bibliographic record
Abstract
The risk of hepatitis C virus (HCV) transmission to surgeons is related to the HCV prevalence in the surgical patient population. As HCV-related cirrhosis is the commonest indication for liver transplantation in Europe and North America, liver transplant surgeons are at particular risk. The prevalence of HCV infection in liver transplant surgeons is unknown. The aim of this study was to estimate the prevalence of HCV infection in liver transplant surgeons attending the 9th Congress of the International Liver Transplantation Society using unlinked anonymous testing for HCV. Surgeons attending the conference were invited to complete an anonymised questionnaire regarding their surgical and transplant practice and provide an unlinked anonymised blood spot sample by finger prick. Samples were screened for antibodies to HCV (enzyme-linked immunosorbent assay III, Ortho Diagnostics, Raritan, NJ). Polymerase chain reaction testing for HCV RNA was performed on reactive samples.A total of 117 liver transplant surgeons (79 European, 16 North American, 10 Asian, 9 South American, 3 Australasian) provided a blood spot sample. Two (1.7%) surgeons had antibodies to HCV, 1 (0.8%) had detectable HCV RNA (genotype 1a). Assuming that both infections were acquired during surgery, the estimated maximum rate of HCV transmission is 1 per 743 to 1,045 years of surgical (0.96 to 1.35 HCV transmissions per 1,000 years of general surgical practice) and 449 to 683 years of liver transplant practice (1.46 to 2.23 HCV transmissions per 1,000 years of liver transplantation practice). In conclusion, risk of HCV transmission to liver transplant surgeons appears to be low despite the particular risks associated with frequently operating on HCV infected patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".