Low Rates of Preemptive Kidney Transplantation: A Root Cause Analysis to Identify Opportunities for Improvement
Bibliographic record
Abstract
BACKGROUND: Kidney transplantation success can help improve the quality of life substantially and alleviate much of the physical, social and psychological stress that comes with dialysis lifestyle. Preemptive kidney transplantation has been established to be superior with more success rates than other means of transplantation types. However, low rates of this method need more attention to address the reason and tackle the root causes affecting low preemptive kidney transplantation. METHODS: This study was undertaken at a tertiary care academic hospital. We conducted a retrospective chart review of 50 living donor kidney transplants (recipient and donor charts) between January 1, 2017 and September 30, 2018 and performed a root cause analysis on the module. A three-step algorithm was developed to divide the root cause of all preemptive kidney transplantation. RESULTS: Out of 50 patients included, only 11 (22%) achieved a successful preemptive kidney transplantation. Furthermore, an estimated glomerular filtration rate (eGFR) of 15 - 20 was a significant factor in achieving preemptive kidney transplantation (P = 0.042). Meanwhile, 22 recipients started dialysis before transplant and was a major cause of not achieving the preemptive transplantation despite them achieving the status. Moreover, the most common recipient root cause was incidental medical issues in 10 cases (20%) while in donor factors, the most common root cause was the delay in evaluating multiple donors to achieve the preemptive status (20%). CONCLUSION: Even though preemptive kidney transplantation is superior when compared to other modalities, achieving such a milestone is still considered very challenging. An eGFR of 15 - 20 was a significant factor in achieving preemptive kidney transplantation, while pre-transplant dialysis was a major cause of not achieving it. Further large-scale studies are needed to identify new factors as well as validate our findings.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".