Cost-Effectiveness of Regular Surveillance Versus Endoscopy at Need for Patients With Barrett’s Esophagus: Economic Evaluation Alongside the Barrett’s Oesophagus Surveillance Study (BOSS) Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND & AIMS: The Barrett's Oesophagus Surveillance Study (BOSS) was the first randomized study of surveillance. This study reports the costs and quality of life outcomes from the BOSS trial and models the outcomes and cost-effectiveness of surveillance beyond the follow-up period of the BOSS study. This trial showed similar stages and rates of esophageal cancer in both arms, but the regular surveillance arm did identify more high-grade dysplasia after a median of 12.8 years follow-up. METHODS: We used a decision tree model based on results from BOSS to conduct a cost-effectiveness analysis of costs and quality-adjusted life years (QALYs). A Markov model was used to extrapolate costs and outcomes over a further 10 years after the trial had ended, representing a 22.8-year time horizon. The proportion with high-grade dysplasia and QALYs was derived from the randomized trial. RESULTS: The total costs associated with 2-yearly surveillance was $5309 vs $3182 in the at-need arm. Total QALYs in the 2-yearly endoscopy arm were 8.647 compared with 8.629 in the at-need arm. Compared with at-need endoscopy, 2-yearly surveillance costs $115,563/QALY gained. In the sensitivity analyses around assumptions on the proportion of high-grade dysplasia that is undetected in the at-need endoscopy arm, surveillance had an incremental cost effectiveness ratio of $94,513/QALY for the best-case and $146,272/QALY for the worst-case scenario. CONCLUSION: Barrett's esophagus surveillance every 2 to 3 years is unlikely to be a cost-effective strategy. Guidelines should take this into account when deciding surveillance intervals.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".