Clinical trial: knowledge of negative <i>Helicobacter pylori</i> status reduces subsequent dyspepsia‐related resource use
Bibliographic record
Abstract
BACKGROUND: Screening for Helicobacter pylori reduces dyspepsia and dyspepsia-related costs in positive individuals. AIMS: To assess effect of knowledge of H. pylori status on healthcare-seeking in negative individuals. METHODS: H. pylori-negative subjects in a community screening programme were randomized to placebo triple therapy or informed of their negative H. pylori status. Dyspepsia-related resource data were extracted from primary care records at 2 years, and National Health Service reference costs were applied to calculate the total cost per subject. Proportions of individuals incurring any cost were compared using a relative risk (RR) and 95% confidence interval (CI). Differences in costs were compared using an independent sample t-test. RESULTS: A total of 1353 H. pylori-negative individuals were randomized to placebo whilst 1355 were informed of their infection status. In the placebo arm, 212 (16%) subsequently incurred any dyspepsia-related cost compared to 172 (13%) informed of their infection status (RR of incurring cost = 0.81; 95% CI: 0.67-0.97). Those informed of their infection status incurred lower costs (mean saving per individual = pound 11.02; 95% CI: - pound 3.52 to 25.56). CONCLUSIONS: H. pylori-negative individuals informed of infection status sought health care for dyspepsia less often than those who were unaware. Population screening may reduce dyspepsia-related costs in uninfected, as well as infected individuals.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".