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Steroid sensitive nephrotic syndrome as a potential contributor to pediatric-onset psychosis: a case-based hypothesis

2025· article· en· W4414619662 on OpenAlexaff
Parinda Parikh, Ananya Reddy Dadem, Dilnuer Wubuli, Isa Gultekin, Rithika Narravula, Arushi Chandra-Kaushik, Avish Chandra, Ishant Buddhavarapu, Mina Oza

Bibliographic record

VenueInternational Journal of Research in Medical Sciences · 2025
Typearticle
Languageen
FieldMedicine
TopicNeurological Complications and Syndromes
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsDiscontinuationPsychosisEtiologyNephrotic syndromePathophysiology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.090
GPT teacher head0.458
Teacher spread0.368 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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