Sex and Gender Considerations in Transplant Research: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: In response to the promotion of sex and gender integration in health-related research, we conducted a scoping review evaluating to what extent sex and gender were considered in the transplantation literature. METHODS: We searched Medline and Embase for manuscripts published between January 1946 and October 2016. Two reviewers independently selected manuscripts describing clinical research on stem cells, tissues, or solid organ transplantation with ≥20 participants, which mentioned "sex" and/or "gender" in the title or abstract. For each eligible manuscript, 2 of 5 reviewers extracted data on study design, population (transplant candidates, recipients, donors), transplant type, and study outcomes. We evaluated whether the terms "sex" and "gender" were applied according to their correct definitions and how these variables were handled at the level of study design and analysis. RESULTS: Of 7565 search results, 2107 manuscripts met the inclusion criteria. Sex and gender were applied interchangeably in more than half of the studies (57.5%). Rarely were sex or gender, when applied correctly, considered in the primary study question (13.3% and 25.0%, respectively). The majority of the studies considered these variables as confounders (74.6% for sex and 68.2% for gender), and a minority considered them as effect measure modifiers (2.8% for sex and 5.0% for gender). CONCLUSIONS: Despite a growing awareness of the need to integrate sex and gender in health research, education is required to ensure accurate and meaningful consideration of these concepts. We outline strategies for integrating sex and gender in allotransplantation and donation research during study design and analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.110 | 0.315 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.037 | 0.036 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".