Sero-prevalence of Helicobacter pylori CagA immunoglobulin G antibody, serum pepsinogens and haemoglobin levels in adults
Bibliographic record
Abstract
Associations observed of Helicobacter pylori infection with haemoglobin levels are inconsistent. We examined associations of H. pylori sero-prevalence and serum pepsinogens (PGs), as non-invasive markers of atrophic gastritis, with haemoglobin levels. A cross-sectional study was undertaken among 654 Jewish and 937 Arab residents of Jerusalem, aged 25-78 years, randomly selected from Israel's national population registry in age-sex and population strata. Sera were tested for H. pylori IgG, cytotoxin-associated gene A (CagA) antigen IgG antibody and PGs levels. Multivariable models were fitted to account for confounders. Participants with atrophic gastritis (PGI < 30 μg/L or a PGI: PGII < 3.0) had lower haemoglobin levels than those without: beta-coefficient -0.34 (95% CI -0.59, -0.09); in men -0.27 (95% CI -0.67, 0.12), and in women -0.43 (95% CI -0.74, -0.12). Lower haemoglobin levels were noted in persons with CagA antibody than in those H. pylori sero-negative or H. pylori-CagA sero-negative: beta-coefficient -0.14 (95% CI -0.29, 0.01). Anaemia was more common among women and men with than without atrophic gastritis: adjusted OR 2.58 (95% CI 1.48, 4.48) and 1.52 (95% CI 0.59, 3.95), respectively. In conclusion, independent of known correlates, atrophic gastritis and apparently CagA sero-positivity, a marker of H. pylori virulent strains, are associated with lower haemoglobin levels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".