Intravenous Antibiotics in the Management of <i>H. pylori</i> Infection: A Systematic Review
Bibliographic record
Abstract
Background: Helicobacter pylori is implicated in the development of gastritis, ulcers, and various gastric cancers, representing significant morbidity, mortality, and healthcare spending. Patients with H. pylori infection have traditionally been treated with oral antibiotics, however, oral therapy is not feasible in all clinical situations. We examined the available evidence supporting the use of intravenous (IV) antibiotics in H. pylori . Methods: This systematic review was carried out by reviewing multiple electronic databases: MEDLINE, CENTRAL, EMBASE, CINAHL, Clinicaltrials.org, and the World Health Organization (WHO) database of clinical trials. Articles published from database inception until February 12, 2023 that discussed the use of IV antibiotics in H. pylori management were included. Results: The search strategy identified 978 studies, with 11 meeting the inclusion criteria. The results demonstrate that there is a lack of robust trials examining the use of IV antibiotics in H. pylori management. Many trials demonstrated that IV antibiotics were safe and efficacious but the results are limited by inconsistencies in the year and geographic location trials were conducted, the IV and oral antibiotic regimens, and the duration of therapy. Conclusions: IV antibiotics appear to be a feasible therapeutic alternative in the management of H. pylori and can be considered, especially in patient populations where oral therapy is contraindicated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".