Platelet count response to H. pylori treatment in patients with immune thrombocytopenic purpura with and without H. pylori infection: a systematic review
Bibliographic record
Abstract
Eradication of H. pylori improves thrombocytopenia in some patients with immune thrombocytopenic purpura by mechanisms that remain obscure. Platelet count responses may occur independently of H. pylori infection as a result of the immune modulating effects of macrolide antimicrobials or the removal of other commensal bacteria. We performed a systematic review of the literature to determine the effect of H. pylori eradication therapy in patients with immune thrombocytopenic purpura by comparing the platelet response in patients who were, and who were not infected with H. pylori. MEDLINE, EMBASE, Cochrane central registry and abstracts from the American Society of Hematology (from 2003) were searched in duplicate and independently without language or age restrictions. Eleven studies, 8 from Japan, were included enrolling 282 patients with immune thrombocytopenic purpura who received eradication therapy; 205 were H. pylori-positive and 77 were H. pylori-negative. The odds of achieving a platelet count response following eradication therapy were 14.5 higher (95% confidence interval 4.2 to 83.0) in patients with H. pylori infection (51.2% vs. 8.8%). No study reported bleeding or quality of life. Adverse events were reported in 12 patients. H. pylori eradication therapy was of little benefit for H. pylori-negative patients. These findings strengthen the causal association between H. pylori infection and immune thrombocytopenia in some patients. Randomized trials are needed to determine the applicability of H. pylori eradication therapy across diverse geographical regions.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".