Bibliographic record
Abstract
Nonsteroidal anti-inflammatory drugs (NSAIDs) are widely prescribed for pain management and inflammation. Acetylsalicylic acid (ASA) is a commonly used treatment for cardiovascular diseases including acute coronary syndromes. Reactions to NSAIDs can vary widely, ranging from exacerbation of underlying cutaneous and respiratory conditions to anaphylaxis and delayed hypersensitivity reactions (DHRs). A thorough clinical history is essential for diagnosing NSAID hypersensitivity, with a focus on the systems involved, reaction timing, and the presence and control of comorbid allergic conditions. Although commonly referred to as an allergy, the mechanisms behind these reactions are not solely IgE-mediated. As such, the authors will primarily use the term “hypersensitivity reactions” in concordance with the latest American Academy of Allergy, Asthma and Immunology (AAAAI) drug allergy practice parameter. While NSAID hypersensitivity reactions may cross-react among cyclooxygenase-1 (COX-1) inhibitors, reactions to selective cyclooxygenase-2 (COX-2) inhibitors are rare and they are typically well tolerated as alternative agents.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.049 | 0.027 |
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".