More economic 25 mg <sup>13</sup>C‐urea breath test can be effective in detecting primary <i>Helicobacter pylori</i> infection in children
Bibliographic record
Abstract
BACKGROUND AND AIM: The high cost of the 13C-urea breath test (UBT) limits its wide application for both epidemiological and clinical studies for diagnosing Helicobacter pylori infection. This study examined if a lower-dose UBT, applying 1 mg/kg of bodyweight (maximum 25 mg, UBT(25)), could introduce cost savings while preserving high diagnostic yields for primary H. pylori infection. METHODS: Children aged less than 16 years were recruited after obtaining consent. Those children with administration of antibiotics or proton pump inhibitors within 1 month of the tests were excluded. Positive tests for both the UBT with 50 mg urea (UBT(50)) and the H. pylori stool antigen (HpSA) were qualifying criteria for H. pylori infection. Negative results for both indicated non-infection. The UBT(25) was conducted 1 week after the UBT(50). The cut-off points for the UBT(25) ranging from 2delta to 5delta were examined for their sensitivity, specificity and accuracy rates. RESULTS: A total of 153 children were recruited (55% male; mean age 9.1 +/- 3.5 years). Both the UBT(50) and HpSA test were positive in 18 (13.1%) and negative in 119 children, respectively. The sensitivity and specificity of the UBT(25) were optimally achieved at 88.9% (95% confidence interval [CI]: 71.4-100) and 95.0% (95% CI: 91.1-99.9), judged with a cut-off point at 3.5delta. The diagnostic accuracy was significantly higher for children older than 7 years than for those younger than 7 years (98%vs 85%, P = 0.009). CONCLUSION: Lower-dose UBT titration by bodyweight can cut costs while maintaining a highly reliable method to screen primary H. pylori infection in children older than 7 years, which is generally beyond school age.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".