End-Stage Renal Disease in a 29-Year-Old Male With Aneurysmal Arteriovenous Fistulas Status Post-Right-Kidney Transplant: A Case Report
Bibliographic record
Abstract
The occurrence of renal failure is higher among African Americans in comparison to individuals of other descents, indicating a disproportionate representation. Chronic kidney disease (CKD) poses a significant healthcare burden that disproportionately affects low-income and minority communities. There are various factors that drive the progression and deterioration of CKD to its advanced stages. These factors include genetic predispositions, socioeconomic status, barriers to medical care, and the patients' own health beliefs and behaviors which impact their screening, risk factor control, and adherence to treatment. Earlier detection and management of hypertension can slow or halt the progression of CKD. This case report is on a case of a 29-year-old African American male with end-stage renal disease (ESRD) status-post right renal transplant. At 21 years old, the patient was diagnosed with benign essential hypertension which progressed from CKD to ESRD. Furthermore, at the age of 23 years old, he was requiring right renal transplants. We aim to shed light on the underlying predispositions that put this young patient at risk for CKD and related comorbidities. Lastly, to highlight dialysis-related complications from the treatment of ESRD and the impact of chronic illness on this patient's overall health.
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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 | 0.002 |
| 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 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".