Ibuprofen-Induced Renal Tubular Acidosis: Case Report on a Not-So-Basic Clinical Conundrum
Bibliographic record
Abstract
Rationale: Renal tubular acidosis (RTA) is a cause of non-anion gap metabolic acidosis (NAGMA) that is infrequently diagnosed and is due to various underlying etiologies that impair the kidney's ability to retain bicarbonate or excrete acid. Ibuprofen is an over-the-counter non-steroidal anti-inflammatory medication that is used by patients widely for a variety of reasons. Although it is well known that ibuprofen and other non-steroidal anti-inflammatory drugs may have nephrotoxic effects, the role of ibuprofen as a cause of RTA and hypokalemia is not well recognized. Presenting Concerns: A 66-year-old man with chemotherapy-treated lymphoma in remission and ongoing heavy ibuprofen use for chronic pain presented to hospital with a 1-week history of increasing lethargy and otherwise unremarkable review of systems. Investigations showed acute kidney injury, hypokalemia, hyperchloremia, and NAGMA with elevated urinary pH and positive urine anion gap. Diagnoses: The final diagnosis of distal RTA secondary to ibuprofen was made after ruling out gastrointestinal bicarbonate loss and additional secondary causes of RTA, including other medications, autoimmune conditions, and obstructive uropathy. Interventions: The patient was admitted and treated with intravenous sodium bicarbonate for 24 hours with correction of hypokalemia via oral supplementation. His ibuprofen-containing medication was discontinued. Outcomes: His acute kidney injury and electrolyte abnormalities resolved within 48 hours of initiating treatment with concurrent resolution of his lethargy. He was discharged home and advised to stop taking ibuprofen. Lessons Learned: We report a case of patient with hypokalemia and NAGMA secondary to ibuprofen and highlight the importance of monitoring for this side effect in patients taking ibuprofen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".