Bibliographic record
Abstract
Annual prevalence estimates of peptic ulcer disease range between 0·12% and 1·5%. Peptic ulcer disease is usually attributable to Helicobacter pylori infection, intake of some medications (such as aspirin and non-steroidal anti-inflammatory medications), or being critically ill (stress-related), or it can be idiopathic. The clinical presentation is usually uncomplicated, with peptic ulcer disease management based on eradicating H pylori if present, the use of acid-suppressing medications-most often proton pump inhibitors (PPIs)-or addressing complications, such as with early endoscopy and high-dose PPIs for peptic ulcer bleeding. Special considerations apply to patients on antiplatelet and antithrombotic agents. H pylori treatment has evolved, with the choice of regimen dictated by local antibiotic resistance patterns. Indications for primary and secondary prophylaxis vary across societies; most suggest PPIs for patients at highest risk of developing a peptic ulcer, its complications, or its recurrence. Additional research areas include the use of potassium-competitive acid blockers and H pylori vaccination; the optimal approach for patients at risk of stress ulcer bleeding requires more robust determinations of optimal patient selection and treatment selection, if any. Appropriate continuation of PPI use outweighs most possible side-effects if given for approved indications, while de-prescribing should be trialled when a definitive indication is no longer present.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".