Photodynamic Therapy for Barrett’s Esophagus with High-Grade Dysplasia: A Cost-Effectiveness Analysis
Bibliographic record
Abstract
OBJECTIVES: To assess the cost-effectiveness of photodynamic therapy (PDT) and esophagectomy (ESO) relative to surveillance (SURV) for patients with Barrett's esophagus (BE) and high-grade dysplasia (HGD). METHODS: A Markov decision tree was constructed to estimate costs and health outcomes of PDT, ESO and SURV in a hypothetical cohort of male patients, 50 years of age, with BE and HGD. Outcomes included unadjusted life-years (LYs) and quality-adjusted LYs (QALYs). Direct medical costs (2003 CDN$) were measured from the perspective of a provincial ministry of health. The time horizon for the model was five years (cycle length three months), and costs and outcomes were discounted at 3%. Model parameters were assigned unique distributions, and a probabilistic analysis with 10,000 Monte Carlo simulations was performed. RESULTS: SURV was the least costly strategy, followed by PDT and ESO, but SURV was also the least effective. In terms of LYs, the incremental cost-effectiveness ratios were 814 dollars/LY for PDT versus SURV and 3,397 dollars/LY for ESO versus PDT. PDT dominated ESO for QALYs in the base-case. The incremental cost-effectiveness ratio of PDT versus SURV was 879 dollars/QALY. In probabilistic analysis, PDT was most likely to be cost-effective at willingness-to-pay (WTP) values between 100 dollars/LY and 3,500 dollars/LY, and ESO was most likely to be cost-effective for WTP values over 3500 dollars/LY. For quality-adjusted survival, PDT was most likely to be cost-effective for all WTP thresholds above 1,000 dollars/QALY. The likelihood that PDT was the most cost-effective strategy reached 0.99 at a WTP ceiling of 25,000 dollars/QALY. CONCLUSIONS: In male patients with BE and HGD, PDT and ESO are cost-effective alternatives to SURV.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 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 | 0.000 |
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".