Bibliographic record
Abstract
To the Editor: Drs. Sharif and Montgomery bring to our attention that the unintended consequences of heightened regulatory oversight are unfortunately having negative effects on patients and donors in the United Kingdom as well as the United States.1Sharif A, Montgomery RA. Regulating the risk-reward trade-off in transplantation [published online ahead of print 2019]. Am J Transplant. https://doi.org/10.1111/ajt.15882Google Scholar Personal communication with transplant professionals in other countries such as Canada, Brazil, India, and Mexico reveal that currently there is not as much concern for severe outcome regulations in all countries. The central theme of both Drs. Sharif and Montgomery’s letter and my original viewpoint is that the current oversight regulation has been shown to harm patients by decreasing the potential number of transplants performed in end-stage organ disease patients.1Sharif A, Montgomery RA. Regulating the risk-reward trade-off in transplantation [published online ahead of print 2019]. Am J Transplant. https://doi.org/10.1111/ajt.15882Google Scholar,2Andreoni KA. Now is the time for the Organ Procurement and Transplantation Network to change regulatory policy to effectively increase transplantation in the United States; Carpe Diem [published online ahead of print 2019]. Am J Transplant. https://doi.org/10.1111/ajt.15759Google Scholar The current push toward normative outcomes is a race by many centers to minimize their program’s exposure to risk of patient and graft loss. Drs. Sharif and Montgomery bring up the concept of developing new metrics that may include reducing mortality and organ discard. I both agree with this concept and am also concerned that most new metrics I have seen discussed have their own significant issues for negative influences on patients and programs. An even more disruptive concept may be to completely eliminate public “flagging” of transplant centers unless detailed Membership Professional and Standards Committee (MPSC) investigation finds there to be true patient outcome issues. Patient and graft outcomes will still be followed for safety concerns and regulatory public reporting. But reporting regulations do not define how a “flag” is determined and our current use of simple statistical difference has been shown to be too frequent and random with far too many programs “flagging” over short time periods. Our current system that myopically focuses on punishment of centers whose outcomes are “statistically” different from others appears to be based more on past paranoia than any actual harm to patients.3Schold JD Miller CM Henry ML et al.Evaluation of flagging criteria of United States kidney transplant center performance: how to best define outliers?.Transplantation. 2017; 101: 1373-1380Crossref PubMed Scopus (18) Google Scholar The MPSC would still be able to query centers with outcomes that they feel are not equivalent to the majority. But the removal of the “flag” would take both programmatic and personal negative pressure off transplant centers and their professionals. In simple terms, no other professional community has self-oversight, which deems one-third of their members underperformers in a 3-year period. Any quality professional would acknowledge that such a metric cannot be useful—and in fact, we now see how much real harm to patients this system has created. Transplant patients and professionals directly involved in delivering transplantation care need to drive the removal of these destructive current metrics and creation of useful ones that allow for increased transplantation and innovation in US transplant centers. A 1-year renal allograft deceased donor graft failure rate of 5.3%, down from 9.2% 10 years ago, is remarkable in a very ill patient population with a high prevalence of profound socioeconomic challenges. This outcome is even more spectacular when considering that death on the waiting list without transplant is nearly the same at 5.06% for all patients on the waiting list in 2018.42018 Annual Data Report. Scientific Registry of Transplant Recipients. http://srtr.transplant.hrsa.gov/annual_reports/Default.aspx. Accessed March 23, 2020.Google Scholar We have reached the point of perfection being the enemy of good for our transplant candidates both in the United States and unfortunately in other countries such as the United Kingdom.
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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 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.019 | 0.032 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".