Bibliographic record
Abstract
Helicobacter pylori colonises the gastric mucosa and is associated with various gastric diseases, including stomach cancer. At least 1 million new cases of stomach cancer cases are reported annually, and it is the fifth top cancer-killer in the world. Although H. pylori can be eradicated by a combination of antibiotics, the treatment success rate is declining due to the rise of antibiotic resistance. The same antibiotic combination must not be prescribed repeatedly. Susceptibility guided precision medicine is the most effective strategy to combat antibiotic resistant H. pylori cases. In addition, maintaining a stomach pH ≥6 during the antibiotic treatment is an important factor to increase cure rates. The new type of acid blocker, P-CABs, have shown promising results in H. pylori treatment. Natural products may suppress the H. pylori growth or relieve the symptoms but have not been successful in solving the root of the problem. New combination therapies show promise and the dream of 100% cure of the infection with minimal side effects from treatment seems achievable. The next decade will see combination therapies with newer acid blockers in widespread use at reasonable cost.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.025 |
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".