RWD128 INCORPORATING REAL-WORLD DATA AND MATHEMATICAL MODELING TO ESTIMATE THE UNDIAGNOSED CHRONIC HEPATITIS B POPULATION
Bibliographic record
Abstract
Objectives: Herpes zoster (HZ) poses a considerable health risk to immunocompromised individuals, including kidney transplant recipients.Preventive strategies, such as immunization with the zoster live vaccine (ZVL) or recombinant zoster vaccine (RZV), may help lower this risk.This study aimed to assess the cost-effectiveness of both vaccines in kidney transplant recipients in Thailand.Methods: A Markov model was developed from a societal perspective to evaluate three strategies: no vaccination, ZVL, and RZV.The model simulated a hypothetical cohort of kidney transplant recipients starting at age 47, using 1-year cycles over a lifetime horizon.Health states included healthy, HZ, postherpetic neuralgia, and death.Input data were drawn from published literature, real world local epidemiological data, and expert clinical opinion.All costs were based on 2023 Thai-specific data.It projected clinical events, such as HZ and postherpetic neuralgia, and associated costs and quality-adjusted life years (QALYs), with discount rate of 3%.Incremental cost-effectiveness ratios (ICERs) were estimated using Thailand's willingness-to-pay (WTP) threshold of THB 160,000 per QALY gained.Deterministic and probabilistic sensitivity analyses were conducted, along with the generation of cost-effectiveness acceptability curves (CEACs).Results: In the base-case analysis, ZVL and RZV had ICERs of THB 30,694 and THB 274,869 per QALY gained, respectively, compared to no vaccination.If the cost of RZV was reduced by 45%, its ICER would fall to THB 153,112 per QALY, below the WTP threshold.At current prices, ZVL had a 99.97% chance of being cost-effective versus 0.03% for RZV.One-way sensitivity analysis identified vaccine efficacy, hospitalization rate, and length of stay as the most influential parameters.Conclusions: ZVL is cost-effective under current conditions in Thailand.Although RZV offers greater clinical protection, its high cost limits affordability.A price reduction would improve its cost-effectiveness, supporting its broader use in kidney transplant recipients.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".