Adrenal insufficiency in pediatric kidney transplantation recipients
Bibliographic record
Abstract
BACKGROUND: Immunosuppression of pediatric kidney transplant (PKT) recipients often includes corticosteroids. Prolonged corticosteroid exposure has been associated with secondary adrenal insufficiency (AI); however, little is known about its impact on PKT recipients. METHODS: This was a retrospective cohort review of PKT recipients to evaluate AI prevalence, risk factors, and adverse effects. AI risk was assessed using morning cortisol (MC) and diagnosis confirmed by an ACTH stimulation test. Potential risk factors and adverse effects were tested for associations with MC levels and AI diagnosis. RESULTS: Fifty-one patients (60.8% male, age 7.4 (IQR 3.8, 13.1) years; 1 patient counted twice for repeat transplant) were included. Patients at risk for AI (MC < 240 nmol/L) underwent definitive ACTH stimulation testing, confirming AI in 13/51 (25.5%) patients. Identified risk factors for AI included current prednisone dosage (p = .001), 6-month prednisone exposure (p = .02), daily prednisone administration (p = .002), and rejection episodes since transplant (p = .001). MC level (2.5 years (IQR 1.1, 5.1) post-transplant) was associated with current prednisone dosage (p < .001), 6-month prednisone exposure (p = .001), daily prednisone administration (p = .006), rejection episodes since transplant (p = .003), greater number of medications (β = -16.3, p < .001), 6-month hospitalization days (β = -3.3, p = .013), creatinine variability (β = -2.4, p = .025), and occurrence of acute kidney injury (β = -70.6, p = .01). CONCLUSION: Greater corticosteroid exposure was associated with a lower MC level and confirmatory diagnosis of AI noted with an ACTH stimulation test. Adverse clinical findings with AI included greater medical complexity and kidney function lability. These data support systematic clinical surveillance for AI in PKT recipients treated with corticosteroids.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".