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Record W4377141777 · doi:10.1016/j.ajt.2023.05.010

American Society of Transplant Surgeons-American Society of Transplantation report of FDA meeting on regulatory expectations for xenotransplantation products

2023· article· en· W4377141777 on OpenAlexaff
Andrew Adams, Linda C. Cendales, David K.C. Cooper, Emanuele Cozzi, John S. Gill, Eric Judd, Eliezer Katz, Allan D. Kirk, Jay A. Fishman, Peter P. Reese, Anji Wall, James F. Markmann

Bibliographic record

VenueAmerican Journal of Transplantation · 2023
Typearticle
Languageen
FieldMedicine
TopicXenotransplantation and immune response
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineXenotransplantationTransplantationFood and drug administrationClinical trialRegulatory affairsEthical issuesRegulatory authorityIntensive care medicineAdvisory committeeFamily medicineEngineering ethicsSurgeryPublic administrationPathologyPharmacologyPolitical science

Abstract

fetched live from OpenAlex

In June 2022, the US Food and Drug Administration Center for Biologics Evaluation and Research held the 73rd meeting of the Cellular, Tissue, and Gene Therapies Advisory Committee for public discussion of regulatory expectations for xenotransplantation products. The members of a joint American Society of Transplant Surgeons/American Society of Transplantation committee on xenotransplantation compiled a meeting summary focusing on 7 topics believed to be key by the committee: (1) preclinical evidence supporting progression to a clinical trial, (2) porcine kidney function, (3) ethical aspects, (4) design of initial clinical trials, (5) infectious disease issues, (6) industry perspectives, and (7) regulatory oversight.

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)0.021
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.030
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.001
Science and technology studies0.0050.001
Scholarly communication0.0080.002
Open science0.0030.002
Research integrity0.0300.014
Insufficient payload (model declined to judge)0.0070.003

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 head0.025
GPT teacher head0.305
Teacher spread0.280 · 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 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".

Quick stats

Citations16
Published2023
Admission routes1
Has abstractno

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