Bibliographic record
Abstract
The presence of gastric acid plays a critical role in the mechanisms of NSAIDs/aspirin-associated gastric and duodenal mucosal injury and ulceration. The role of gastric acid and its relationship to NSAIDs/aspirin in mucosal damage, ulcer and ulcer complications continues to be an important concern because of the increasing worldwide use of NSAIDs and aspirin. Acid suppression continues to be an important prevention strategy for NSAID-associated gastric and duodenal ulcer and ulcer complications. While a coxib or an NSAID and PPI in combination are considered to have comparable safety profiles, the evidence from direct comparisons in high-risk patients is limited, and the cardiovascular safety of coxibs and NSAIDs remains a concern especially in patients with a high risk of cardiovascular disease. An evaluation of individual gastrointestinal and cardiovascular risks and benefits, selection of the most appropriate NSAID and dose for each particular patient should always be emphasized. Twice daily PPI is more appropriate to protect a patient who is taking NSAIDs twice daily. PPI co-therapy is still recommended in patients receiving dual antiplatelet treatment, although conflicting results have been reported about adverse drug interactions between PPIs and clopidogrel.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.004 | 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".