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Record W3173903288 · doi:10.1097/sla.0000000000004994

Liver Transplantation in the Time of a Pandemic

2021· article· en· W3173903288 on OpenAlexaff
Malcolm MacConmara, Benjamin Wang, Madhukar S. Patel, Christine Hwang, Lucia DeGregorio, Jigesh A. Shah, Steven I. Hanish, Dev M. Desai, Raymond Lynch, Bekir Tanrıöver, Herbert J. Zeh, Parsia A. Vagefi

Bibliographic record

VenueAnnals of Surgery · 2021
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicinePandemicLiver transplantationTransplantationLogistic regressionDemographyOdds ratioDemographicsInternal medicineCoronavirus disease 2019 (COVID-19)DiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

OBJECTIVE: During the initial wave of the COVID-19 pandemic, organ transplantation was classified a CMS Tier 3b procedure which should not be postponed. The differential impact of the pandemic on access to liver transplantation was assessed. SUMMARY BACKGROUND DATA: Disparities in organ access and transplant outcomes among vulnerable populations have served as obstacles in liver transplantation. METHODS: Using UNOS STARfile data, adult waitlisted candidates were identified from March 1, 2020 to November 30, 2020 (n = 21,702 pandemic) and March 1, 2019 to November 30, 2019 (n = 22,797 pre-pandemic), and further categorized and analyzed by time periods: March to May (Period 1), June to August (Period 2), and September to November (Period 3). Comparisons between pandemic and pre-pandemic groups included: Minority status, demographics, diagnosis, MELD, insurance type, and transplant center characteristics. Liver transplant centers (n = 113) were divided into tertiles by volume (small, medium, large) for further analyses. Multivariable logistic regression was fitted to assess odds of transplant. Competing risk regression was used to predict probability of removal from the waitlist due to transplantation or death and sickness. Additional temporal analyses were performed to assess changes in outcomes over the course of the pandemic. RESULTS: During Period 1 of the pandemic, Minorities showed greater reduction in both listing (-14% vs -12% Whites), and transplant (-15% vs -7% Whites), despite a higher median MELD at transplant (23 vs 20 Whites, P < 0.001). Of candidates with public insurance, Minorities demonstrated an 18.5% decrease in transplants during Period 1 (vs -8% Whites). Although large programs increased transplants during Period 1, accounting for 61.5% of liver transplants versus 53.4% pre-pandemic (P < 0.001), Minorities constituted significantly fewer transplants at these programs during this time period (27.7% pandemic vs 31.7% pre-pandemic, P = 0.04). Although improvements in disparities in candidate listings, removals, and transplants were observed during Periods 2 and 3, the adjusted odds ratio of transplant for Minorities was 0.89 (95% CI 0.83-0.96, P = 0.001) over the entire pandemic period. CONCLUSIONS: COVID-19's effect on access to liver transplantation has been ubiquitous. However, Minorities, especially those with public insurance, have been disproportionately affected. Importantly, despite the uncertainty and challenges, our systems have remarkable resiliency, as demonstrated by the temporal improvements observed during Periods 2 and 3. As the pandemic persists, and the aftermath ensues, health care systems must consciously strive to identify and equitably serve vulnerable populations.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.155
GPT teacher head0.328
Teacher spread0.173 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2021
Admission routes1
Has abstractyes

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