Cost-Effectiveness Analysis of Different Testing Strategies that Use Antibody Levels to Detect Chronic Hepatitis C in Blood Donors
Bibliographic record
Abstract
AIM: We conducted a cost-effectiveness analysis of seven hepatitis C virus (HCV) testing strategies in blood donors. METHODS: Three of the seven strategies were based on HCV diagnosis and reporting guidelines in Mexico and four were from previous and current recommendations outlined by the CDC. The strategies that were evaluated determine antibody levels according to the signal-to-cut-off (S/CO) ratio and use reflex Immunoblot (IMB) or HCV RNA tests to confirm true positive (TP) cases of chronic HCV infection. Costs were calculated from the perspective of the Mexican Institute of Social Security (IMSS). A decision tree model was developed to estimate the expected number of true positive cases and costs for the base-case scenarios and for the sensitivity analyses. RESULTS: Base-case findings indicate an extended dominance of the CDC-USA2 and CDC-USA4 options by the IMSS Mexico3 and IMSS-Mexico1 alternatives. The probabilistic sensitivity analyses results suggest that for a willingness-to-pay (WTP) range of $0-9,000 USD the IMSS-Mexico1 strategy is the most cost-effective of all strategies ($5,000 USD per TP). The IMSS-Mexico3, IMSS-Mexico2, and CDC-USA3 strategies are also cost-effective strategies that cost between $7,800 and $8,800 USD per TP case detected. The CDC-USA1 strategy was very expensive and not cost-effective. CONCLUSIONS: HCV antibody testing strategies based on the classification of two or three levels of the S/CO are cost-effective procedures to identify patients who require reflex IMB or HCV RNA testing to confirm chronic HCV infection.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".