An update of laboratory diagnosis of Helicobacter pylori in the Kingdom of Saudi Arabia
Bibliographic record
Abstract
Helicobacter pylori is a micro-aerophilic, slow-growing, Gram-negative spiral bacterium that colonizes the mucous lining of the human stomach. Infection with this bacterium has been identified as a cause of gastritis, peptic ulcer disease, and gastric mucosa-associated lymphoid tissue lymphoma. Globally, the prevalence of H. pylori-related infection is high compared to any other infectious diseases, and the rate of prevalence much higher in developing countries than in developed nations. This review article aims to describe the trend of H. pylori-related works in the Kingdom of Saudi Arabia (KSA) and the use of various laboratory tests for the diagnosis of H. pylori-related infections in adults and children. Therefore, published literature was referenced in the explanation and discussion of the different methods used to diagnose H. pylori-related disease, including papers published in the KSA and other Middle Eastern countries. The PubMed (http://www.ncbi.nlm.nih.gov/pubmed?cmd=search) search engine was used extensively. Culture and histopathology tests have been employed widely to detect this pathogen at the early stage. However, over the years, an array of tests including the rapid urease test, serology, the urea breath test, the fecal antigen test, and molecular testing have been developed to diagnose and better manage H. pylori-associated diseases since the discovery of this novel pathogen.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".