Meta‐analysis: <i>Helicobacter pylori</i>‘test and treat’ compared with empirical acid suppression for managing dyspepsia
Bibliographic record
Abstract
BACKGROUND: Which of Helicobacter pylori'test and treat' or empirical acid suppression should be preferred for the initial management of uncomplicated dyspepsia is controversial. Aim To conduct an individual patient data meta-analysis of randomized controlled trials (RCTs) of 'test and treat' vs. empirical acid suppression in adults with uncomplicated dyspepsia in primary care. METHODS: Investigators provided original data sets for analysis. Effect of management strategy on symptom status and dyspepsia-related resource use at 12-month follow-up was examined by pooling symptom and cost data to obtain relative risk (RR) of remaining symptomatic at 12 months and weighted mean difference (WMD) in costs between the two strategies with 95% confidence intervals (CI). RESULTS: We identified three eligible RCTs containing 1547 patients, 791 (51%) of whom were assigned to 'test and treat'. There was no difference detected in symptom-cure at 12 months (RR = 0.99; 95% CI: 0.95-1.03). There was a nonsignificant trend towards cost-savings with 'test and treat' (WMD in costs = - 28.91 pound; 95% CI: - 68.48 pound to 10.65 pound). CONCLUSIONS: There was little difference in symptom-resolution or costs between the two strategies. A combination of patient and physician preference should determine the initial approach to the management of uncomplicated dyspepsia.
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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 | 0.022 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.057 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".