Bibliographic record
Abstract
A 77-year-old male was admitted to hospital after a four week course of Piperacillin Tazobactam for a liver abscess, with a diffuse morbiliform rash. He was found to have drug reaction with eosinophilia and systemic symptoms (DRESS). During his in-hospital course, he had a severe upper gastrointestinal bleed (UGIB) and endoscopy revealed a large, circumferential ulcer at the incisura. We present a case of upper gastrointestinal bleed secondary to DRESS and review the literature of DRESS and upper gastrointestinal bleed. A literature search of DRESS and gastrointestinal bleeds was performed on the OVID Medline(R) and PubMed databases. Search terms for DRESS were “DRESS syndrome,” “drug reaction with eosinophilia and systemic symptoms,” “drug rash with eosinophilia and systemic symptoms,” “drug hypersensitivity and eosinophilia,” and “drug-induced hypersensitivity syndrome.” Search terms for gastrointestinal bleed were “upper gastrointestinal bleed,” “gastrointestinal bleed,” “gastrointestinal hemorrhage,” and “peptic ulcer.” There were no limits placed on publication year or language. DRESS is an adverse systemic reaction to a variety of medications, with a high mortality rate. Antiepileptics and allopurinol are the most commonly described causative agents. Gastrointestinal bleed has previously been reported in association with DRESS, although this has only been in the setting of cytomegalovirus (CMV) reactivation. We report the first known case of severe upper gastrointestinal bleed in the setting of DRESS with negative CMV status. We propose the pathogenesis underlying gastrointestinal bleed and DRESS is similar to other inflammatory conditions predisposing patients to upper gastrointestinal bleeds. None
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".