Steroid sensitive nephrotic syndrome as a potential contributor to pediatric-onset psychosis: a case-based hypothesis
Bibliographic record
Abstract
Steroid-sensitive nephrotic syndrome (SSNS) is a type of primary glomerulopathy that results in multiple complications, including edema and hyperlipidemia. Most existing studies indirectly attribute psychiatric manifestations in SSNS to corticosteroid therapy, while its etiological role in neuroinflammation remains rarely discussed. This article aims to explore this association through a unique case report. An investigation of available English-language literature providing insight into pathophysiology of SSNS, neuroinflammation, and psychosis was done. Information collected was reviewed and analysed for quality and relevance. We present a rare case of a 13-year-old male with SSNS, who exhibited escalating oppositional behavior, emotional dysregulation, and aggression, resulting in a parental request for discontinuation of steroids and immunosuppressants. Despite cessation of medication, symptoms progressed to worsening of psychosis with multiple psychiatric hospitalizations, raising concerns for a potential link between SSNS and neuropsychiatric origin. Collateral history revealed discontinuation of immunosuppressant therapy in early childhood, indicating its limited significance in the current presentation. This case elucidates the potential correlation between SSNS and psychosis in pediatric patients from a pathophysiological and neuropsychiatric point of view, necessitating further investigations into its underlying mechanism.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".