Low renal transplantation rates in children with end‐stage kidney disease: A study of barriers in a low‐resource setting
Bibliographic record
Abstract
After 2 decades as a low-cost transplant centre in India, our rates of kidney transplantation are low compared to the burden of end-stage kidney disease (ESKD). We performed this study to identify possible barriers inhibiting paediatric kidney transplant and to assess the outcomes of paediatric ESKD. A retrospective chart review of ESKD patients (2013 - 2018) at a tertiary paediatric nephrology centre was conducted. Medical/non-medical barriers to transplant were noted. Patient outcomes were classified as "continued treatment," "lost to follow-up (LTFU)" or "died." Of 155 ESKD patients (monthly income 218 USD [146, 365], 94% self-pay), only 30 (19%) were transplanted (28 living donor). Sixty-five (42%) were LTFU, 19 (12%) died, and 71 (46%) continued treatment. LTFU/death was associated with greater travel distance (300 km [60, 400] vs 110 km [20, 250] km, P < .0001) and lower monthly income (145 USD [101, 290] vs 290 USD [159, 681], P < .0001). Among those who continued treatment, 41 proceeded to transplant evaluation of whom 13 had no living donor and remained waitlisted for 27 months (15, 30). The remainder (n = 30) did not proceed to transplant due to unresolved medical issues (n = 10) or a lack of parental interest in pursuing transplant (n = 20). Barriers to transplantation in low-resource setting begin in ESKD. LTFU resulted in withdrawal of care and was associated with low socioeconomic status. Among those who continued treatment, transplant rates were higher but medical challenges and negative attitudes towards transplant and organ donation occurred.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".