HBV screening before adjuvant chemotherapy in patients with early breast cancer: A cost-effectiveness analysis.
Bibliographic record
Abstract
11 Background: The seroprevalence of hepatitis B virus (HBV) infection among Canadian was 0.4%, and 1.6% among immigrants. Most infected individuals have clinically silent disease. Cytotoxic chemotherapy causes reactivation in 30% of the HBV infected patients. This can be severe and fatal, may also lead to interruption of chemotherapy. HBV screening before adjuvant chemotherapy (ADJ) for breast cancer (BC) seems to be a plausible strategy. Our objective is to estimate the health and economic effects of HBV screening strategies. Methods: We developed a state transition microsimulation model to examine the cost effectiveness of 3 strategies for 55 year old BC patients undergoing ADJ: (1) No screen; (2) Screen Imm: Screen immigrant only and treat; (3) Screen all: Screen all and treat; with antiviral therapies. In the model, health states were constructed to reflect the natural history of BC and HBV. Model data were obtained from published literature. We used a payer perspective, a lifetime time horizon, and used a 5% discount rate. Results: Screen all would prevent 43 severe reactivations (SR), 9 deaths from reactivation (DR), 22 chemotherapy interruptions (CI), 36 decompensated cirrhosis (DC), 48 HCCs, and 67 HBV deaths per 100,000 persons screened over the lifetime of the cohort. Screen Imm would prevent 34 SR, 4 DR, 20 CI, 30 DC, 41 HCCs, and 52 HBV deaths. Screen all was associated with an increase of at least 0.00368 quality adjusted life years (QALY) and cost C$116 more per person, translating to an incremental cost effectiveness ratio (ICER) of C$31,518-51,276/QALY gained compared with No screen, depends on different antiviral therapies. Screen all was the most cost effective, while Screen Imm was ruled out due to extended dominance (ED) by No Screen and Screen all. Conclusions: HBV screening before ADJ for BC patients would prevent a significant number of reactivations, and is likely be cost effective. [Table: see text]
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".