Diagnosis of <i>Helicobacter pylori</i> infection: serology vs. urea breath test
Bibliographic record
Abstract
ABSTRACT The objective of the study was to ascertain an optimal Helicobacter pylori diagnostic strategy using population-level laboratory data comparing the performance of serology against urea breath test (UBT). H. pylori diagnostic test results for serology and UBT from two laboratories over a 12-year period (2006–20017) were extracted, linked, and analyzed. A subset of this population underwent both methods of testing within days of each other, enabling a direct comparison of the two methods. The average prevalence of H. pylor i positivity was 21.3% by serology and 17.5% by UBT. There were 2,612 individuals who had serology performed first, followed by UBT within 14 days. For this subset, the sensitivity of serology compared with UBT was 96.5% with a specificity of 79.2%. The negative predictive value for serology was 98.4%. Contrary to various recent clinical guidelines, the data show that serology still has utility as a sensitive enough test to be used as an initial H. pylori screening test in a lower prevalence population. Negative serology can be used with confidence to rule out active infection, whereas a positive serology could be followed up with a UBT or a similar performing test such as stool antigen to differentiate active from past infection. For population-based diagnostic recommendations, such a strategy may be ideal since serology generally costs less than UBT and may be combined with a blood draw being done for other diagnostic tests. Continuing to offer serology increases options for patients and may provide economic benefits for single-payer health care systems or health maintenance organizations. IMPORTANCE This study compares the performance of serology with urea breath test in the diagnosis of Helicobacter pylori in a population-level data set and mimics a head-to-head direct comparison as the study population had both tests performed within 2 weeks of each other. This provides new information supporting the use of serology in a diagnostic algorithm. There are several instances where serology could be preferable to patients to rule out disease, despite some guidelines suggesting serology should not be used.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".