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Record W3202833864 · doi:10.1093/ajcp/aqab143

Nonhormone-Related Histologic Findings in Postsurgical Pathology Specimens From Transgender Persons

2021· review· en· W3202833864 on OpenAlexaff
Archan Kakadekar, Dina N. Greene, Robert L. Schmidt, Mahmoud A. Khalifa, Alicia R. Andrews

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2021
Typereview
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineTransgenderButtocksMEDLINEAnatomical pathologyComplicationSurgeryGeneral surgeryPathology

Abstract

fetched live from OpenAlex

OBJECTIVES: The objective of this review is to characterize the literature addressing postprocedural complications in persons undergoing gender-affirming surgeries. METHODS: A literature search using the OVID MEDLINE and PubMed databases was performed to identify all studies describing histologic findings in surgical pathology specimens from transgender persons from 1946 to April 2021. The studies describing postsurgical complications were categorized based on anatomic site, type of complication, study design, publication region, and date. RESULTS: Thirty-nine studies describing postsurgical complications in transgender women were identified. The most common sites of postprocedural pathology included the breasts and neovagina, with additional studies including buttocks and thighs, cutaneous sites, and the pulmonary system. Most of the literature comprised case reports, followed by case series and comparative studies. The search did not identify any studies of complications secondary to masculinizing surgeries. CONCLUSIONS: This body of literature is small but growing. Most studies are case reports. There are significant gaps in the literature. The literature in this area is not yet mature enough to support a meta-analysis.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.917
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0100.004
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0030.001

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 head0.172
GPT teacher head0.509
Teacher spread0.337 · 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
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

Citations2
Published2021
Admission routes1
Has abstractyes

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