Should Transplant Nephrology Pursue Recognition from the Accreditation Council for Graduate Medical Education (ACGME)?
Bibliographic record
Abstract
Kidney transplant is not only the best treatment for patients with advanced kidney disease but it also reduces health care expenditure. The management of transplant patients is complex as they require special care by transplant nephrologists who have expertise in assessing transplant candidates, understand immunology and organ rejection, have familiarity with perioperative complications, and have the ability to manage the long-term effects of chronic immunosuppression. This skill set at the intersection of multiple disciplines necessitates additional training in Transplant Nephrology. Currently, there are more than 250,000 patients with a functioning kidney allograft and over 100,000 waitlisted patients awaiting kidney transplant, with a burgeoning number added to the kidney transplant wait list every year. In 2022, more than 40,000 patients were added to the kidney wait list and more than 25,000 received a kidney transplant. The Advancing American Kidney Health Initiative, passed in 2019, is aiming to double the number of kidney transplants by 2030 creating a need for additional transplant nephrologists to help care for them. Over the past decade, there has been a decline in the Nephrology-as well Transplant Nephrology-workforce due to a multitude of reasons. The American Society of Transplantation Kidney Pancreas Community of Practice created a workgroup to discuss the Transplant Nephrology workforce shortage. In this article, we discuss the scope of the problem and how the Accreditation Council for Graduate Medical Education recognition of Transplant Nephrology Fellowship could at least partly mitigate the Transplant Nephrology work force crisis.
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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 | 0.015 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.020 | 0.017 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".