Amoxicillin-Induced Eosinophilic Pneumonia with Granulomatous Reaction: Discrepancy between Drug-Induced Lymphocyte Stimulation Test Findings and the Provocation Drug Test
Bibliographic record
Abstract
: A 59-year-old man was admitted to the hospital with pulmonary infiltration, fever, erythema, and eosinophilia. Two weeks before admission, he received amoxicillin, acetaminophen, and shoseiryu-to (a Japanese herbal medicine) for a common cold. Bronchoalveolar lavage was performed, and an increased number of eosinophils was recovered. Transbronchial biopsy specimens showed granuloma and interstitial thickening with eosinophils and lymphocytes. Drug-induced eosinophilic pneumonia was suspected, so all drugs were discontinued. The symptoms and infiltration shadow disappeared. A drug-induced lymphocyte stimulation test (DLST) was positive for acetaminophen but not for amoxicillin. In contrast to the DLST, a provocation test revealed that amoxicillin induced the drug allergy. A very striking observation was the coexistence of pulmonary eosinophilia and granulomatous lung infiltrations. In addition, there was a discrepancy between the DLST and provocation test findings. To our knowledge, there is no previous report of drug-induced eosinophilic pneumonia with a granulomatous reaction.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".