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Record W4396738472 · doi:10.1016/j.ajt.2024.05.002

Considerations in gender-affirming hormone therapy in transgender and gender diverse patients undergoing liver transplantation

2024· review· en· W4396738472 on OpenAlexaff
Newsha Nikzad, Andrew R. Fisher, Anjana Pillai, Laura E. Targownik, Helen S. Te, Andrew Aronsohn, Sonali Paul

Bibliographic record

VenueAmerican Journal of Transplantation · 2024
Typereview
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineTransgenderLiver transplantationGender dysphoriaTransplantationHealth careIntensive care medicineLiver diseasePopulationPerioperativeHormone therapyDiseaseFamily medicineSurgeryInternal medicinePsychology

Abstract

fetched live from OpenAlex

Liver transplantation is lifesaving for patients with end-stage liver disease. Similar to the role of transplantation for patients with end-stage liver disease, gender-affirming hormone therapy (GAHT) can be lifesaving for transgender and gender diverse (TGGD) patients who experience gender dysphoria. However, management of such hormone therapy during the perioperative period is unknown and without clear guidelines. Profound strides can be made in improving care for TGGD patients through gender-affirming care and appropriate management of GAHT in liver transplantation. In this article, we call for the transplant community to acknowledge the integral role of GAHT in the care of TGGD liver transplant candidates and recipients. We review the current literature and describe how the transplant community is ethically obligated to address this health care gap. We suggest tangible steps that clinicians may take to improve health outcomes for this minoritized patient population.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.113
GPT teacher head0.401
Teacher spread0.287 · 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
GenreReview

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

Citations5
Published2024
Admission routes1
Has abstractno

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