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Record W4231304797 · doi:10.1002/lt.25480

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2019· letter· en· W4231304797 on OpenAlexaboutno aff
Raymond Lynch, Fei Ye, Quanhu Sheng, Zhiguo Zhao, Seth J. Karp

Bibliographic record

VenueLiver Transplantation · 2019
Typeletter
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLiver transplantationInternal medicineTransplantation

Abstract

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Potential conflict of interest: Nothing to report. To the Editor: We read with concern the editorial by Hirose1 on our article. We appreciate the opportunity to respond, especially given that the United Network for Organ Sharing (UNOS) Board of Directors recently adopted the concentric circle model we analyzed (the “250/500” model in our article).2 Hirose’s first argument was that because state borders were not designed for a specific use, they cannot function in this role. Yet, state lines serve effectively as the geographic boundary for numerous public functions that intersect with the federal government, including the management of organ donor registries and Medicaid programs. Country borders were not drawn for organ allocation either, but most would accept that the US‐Canada border is a reasonable one to define organ allocation. We presented what we felt was a compelling justification to support the distribution model, and we simply ask that the community consider our arguments. Hirose delved into the minutiae of various scenarios in an attempt to discredit the premise of state‐based distribution. We acknowledge that additional rules or exceptions would be necessary for a fully formed policy using state borders. To require a fully detailed proposal in a concept article seems an unfairly high bar, especially given the number and gravity of the problems that have been identified in UNOS policies. Hirose criticized our model on the basis that the boundaries are arbitrary. We respond that every model must employ some arbitrary boundary. Why is a 250‐mile sharing radius better than one of 251 miles, or why use nautical miles in place of statute miles? A key difference between our state‐based proposal and that espoused by Hirose is that we focused on boundaries that promote access to transplantation for socioeconomically disadvantaged populations, maximize organ donation and utilization, and represent a reasonable compromise between competing interests in a manner compliant with the requirements of the laws governing allocation policies. Hirose further objected that we presented donor service area (DSA)–level projections of changes to liver transplant volume, which he described as a “critical flaw.” Although we acknowledge that precision drops in smaller geographic areas, our main finding regarding the flight of organs from the poor, rural South applies to a huge area spanning multiple states and DSAs. In fact, with a fixed number of organs in play, such losses are a necessity of redistribution models that subsidize the underperforming New York organ procurement organization. We note that the Scientific Registry of Transplant Recipients (SRTR) considers liver simulated allocation model (LSAM) projections regarding the direction of the change in organ availability to be reliable.3 In this, our results were highly concordant with those of the SRTR, so the effect of this new policy on impoverished areas with high wait‐list mortality and lesser wait‐list access is beyond dispute—massive reductions in organ availability.5 This also comports with common sense when looking at SRTR‐generated maps of organ redistribution. Hirose stated that the SRTR “stratified the results” of proposed changes by a cumulative community risk score but failed to mention that if one examines these results, it is quite clear that the acuity circles model results in a significant decrease in the transplant rate for high‐risk, low‐income communities and an increase in the transplant rate for low‐risk, wealthier communities. Our purpose was to maximize organ use and protect socioeconomically disadvantaged regions. These are critical mandates embedded in the Final Rule that the UNOS has failed to resolve. Our commitment to finding solutions is further supported by our publication on a separate novel model of directed sharing that decreases discards compared with other broader sharing models.6 Hirose1 referenced only one aspect of the allocation policy: that it “shall not be based on the candidates’ place of listing.” Other mandates of the Final Rule that supersede the portion quoted by Hirose include preceding sections about the best use of organs, avoiding wastage, promoting patient access to transplantation, and protecting underserved communities.7 It is the desire to maximize organ supply and avoid deaths among already marginalized groups that drives us to oppose poorly designed policies. We have previously written that the push to abolish DSAs as a unit of distribution was more grounded in expediency than necessity.9 We now stand behind state‐based distribution as one of possibly several reasonable alternatives. We do not write to say that state‐based distribution is definitively the best policy our community could develop but rather that it outperforms the acuity circles model in critical areas. Hirose and others have suggested that the acuity circles model is the best means of saving lives.10 Such claims are predicated on reductions in wait‐list mortality under broader sharing policies. The SRTR’s projections for the acuity circles policy show that 21 DSAs will gain 461 livers, preventing 121 wait‐list deaths.12 Among the 30 DSAs that will lose 519 livers, however, LSAM projects that only 7 more wait‐list deaths will occur. Taken as a whole, the model projects that doing 57 fewer liver transplants nationwide will lead to 114 fewer wait‐list deaths. These results strain credulity and are due to the following: The exclusion of those who are removed from the waiting list as being too sick to transplant and almost certainly die thereafter. The failure to model that wait‐list mortality is dependent on geographic factors independent of Model for End‐Stage Liver Disease score. The lack of longterm follow‐up in the modeling. The inherent limitations of LSAM. Of course, even if there were fewer wait‐list mortalities, these are not lives saved, only deaths deferred, leading to even more patients on the waiting list in the future. If we take it as obvious that the best way to save a life is to perform a transplant, it is hard to imagine how a system that leads to fewer transplants could save more lives. This critical flaw was acknowledged by the UNOS board in December 2018.13 Our legacy in transplant depends on how we treat our most vulnerable patients. It is critical that our community be allowed honest and open debate to foster the development of the best possible organ allocation policy for end‐stage liver failure patients. Those in positions of authority and knowledge must use both in a responsible fashion to satisfy this goal.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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.

Opus teacher head0.015
GPT teacher head0.240
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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