Bibliographic record
Abstract
OBJECTIVE: To describe what is believed, as of November 4, 2003, to be the first case published in the literature of acute interstitial nephritis (AIN) due to pantoprazole. CASE SUMMARY: A 77-year-old white woman presented to the hospital with elevated serum creatinine, oliguria for the past 24 hours, arthralgia, fatigue, fever, and bilateral flank pain. The patient had initiated treatment with oral pantoprazole 40 mg/d for gastroesophageal reflux 2 months prior to admission. After 5 weeks of therapy, she stopped taking pantoprazole due to general malaise. Upon admission, all home medications, including pantoprazole, were reinitiated based on the patient's medication list. Serum creatinine increased to 6.1 mg/dL on day 4 of admission from a baseline of 1.0 mg/dL. Pantoprazole therapy was promptly discontinued, and prednisone 40 mg/d was initiated. Urinalysis revealed eosinophils, and a subsequent renal biopsy confirmed a diagnosis of AIN. The serum creatinine level gradually declined over 2 weeks, and the patient was discharged home with a serum creatinine level of 1.6 mg/dL. The Naranjo probability scale suggests a highly probable relationship between AIN and pantoprazole therapy in this patient. DISCUSSION: Drug hypersensitivity reactions are the most common cause of AIN. There have been several reported cases of omeprazole-induced AIN. Although there are very few prospective data on the efficacy of treatment of drug-induced AIN, corticosteroids may have a role in recovery of renal function. Prednisone doses of 1 mg/kg/d have been suggested. CONCLUSIONS: Physicians should be aware that drug-induced AIN can be associated with proton-pump inhibitors. Early detection of this rare adverse reaction may prevent acute renal insufficiency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".