Test-and-Treat Strategies for<i>Helicobacter pylori</i>in Uninvestigated Dyspepsia: A Canadian Economic Anaylsis
Bibliographic record
Abstract
BACKGROUND: Recognition of the pivotal role of Helicobacter pylori in the pathogenesis of peptic ulcer disease has revolutionized primary care approaches to dyspepsia. Decision analysis was used to compare the cost effectiveness of empirical ranitidine with a test and treat strategy using either H pylori serology or the 13carbon-urea breath test (13C-UBT). PATIENTS AND METHODS: A cohort of patients under age 50 years presenting with uninvestigated dyspepsia was evaluated. Three initial strategies were compared with respect to direct medical costs and effectiveness in curing H pylori-related ulcers - empirical ranitidine, H pylori serology and UBT. A one-year time horizon and third-party payer perspective were adopted in a Canadian health care setting. RESULTS: UBT was more costly than either serology or ranitidine but was the most effective strategy and required the fewest endoscopies. No strategy demonstrated dominance over another in the base case. The incremental cost effectiveness ratio (ICER) of serology versus ranitidine was $118/cure, and sensitivity analysis induced dominance of serology in several plausible scenarios. The baseline ICER of UBT versus serology was $885/cure but showed substantial variation in sensitivity analysis. Each ICER was highly sensitive to variation in the cost of the tests themselves. At a serology cost of $25, UBT became dominant when its cost fell to $39. CONCLUSIONS: In low risk patients with uninvestigated dyspepsia, testing for H pylori using serology appears to be economically attractive. 13C-UBT may be a cost effective alternative to serology if local conditions closely approximate the model parameters. Future changes in the costs of serology and 13C-UBT may determine the optimal approach.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".