Cost-effectiveness and long-term impact of Helicobacter pylori ???test and treat??? service in reducing open access endoscopy referrals
Bibliographic record
Abstract
INTRODUCTION: We have shown that the introduction of a carbon urea breath test (13C-UBT) service for Helicobacter pylori screening and eradication is effective in reducing the rate of open access endoscopy referrals in patients aged < 40 years in the short term. This has been substantiated by several randomized controlled trials comparing a 'test and treat' strategy with early endoscopy in these patients. However, the long-term impact of such a strategy is not established. OBJECTIVE: To ascertain the influence of 13C-UBT services on open access endoscopy referral rates in dyspeptic patients under the age of 40 years over a period of 5 years. METHODS: Retrospective analysis of open access endoscopy referral rates between August 1990 and July 2000. Cost minimization analysis was performed with a Decision Analysis Model using Treeage Data 3.5. RESULTS: The total number of open access referrals for endoscopy during 1990-1995 was between 765 and 1325 per year. The proportion of endoscopies performed in patients < 40 years ranged between 33.4% and 34.6%. The total number of endoscopy referrals during 1995-2000 after the introduction of the 13C-UBT services was between 1178 and 1321 per year. However, there was a sustained reduction in the proportion of patients aged < 40 years, ranging between 23.2% and 26.2% (Chi2 = 153.9, degrees of freedom = 9, P < 0.0001) during this period. CONCLUSIONS: The H. pylori screening and treatment strategy using the 13C-UBT service results in a sustained reduction of the number of endoscopy referrals and is cost effective in dyspeptic patients under the age of 40 years, enabling better utilization of available resources.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".