A Case Report of Nephrotic Syndrome While Undergoing Quinine Therapy
Bibliographic record
Abstract
We summarize the case of an 81-year-old Caucasian female who presented to her family physician with signs and symptoms of nephrotic syndrome following a brief exposure to quinine. Prior to that visit, she was clinically well with no chronic medical ailments and met with her family physician for annual physical assessments. She had taken 11 tablets of quinine for nocturnal leg cramps over the course of 28 days before starting to notice mild peripheral edema, which subsequently progressed, leading to a family physician review. Her initial serum albumin level was 12 g/L, and a 24-hour urine protein output was quantified at 8.14 g/day; she was diagnosed as having nephrotic syndrome. A kidney biopsy confirmed the diagnosis of minimal change disease (MCD). Quinine therapy was stopped, and she was initiated on a tapering regime of prednisone with concurrent cyclosporine therapy. Within a fortnight of starting therapy, she went into remission and her immunosuppressive medications were rapidly tapered and discontinued. This paper reports an association between the use of quinine and subsequent MCD. This case report proposes that the use of quinine has an association with, and may be causal for, the development of minimal change disease. As this is yet an unreported adverse effect, this paper seeks to increase the knowledge of the varied and numerous effects of quinine.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".