A Scoping Review Mapping Trans* and Gender Diverse People's Representation in Cancer Research
Bibliographic record
Abstract
INTRODUCTION: Trans* and gender diverse people (TGD) are at risk of experiencing inequities across the cancer continuum. While limited evidence suggests cancer is a concern for TGD people, few systematic reviews or other knowledge syntheses exist that can guide efforts to improve the evidence base and address the inequities TGD people face in cancer care. METHODS: Our team conducted a mixed methods scoping review exploring how cancer affects TGD people. We extracted data on cancer type and phase of the cancer continuum, gender definition operationalization, results, and TGD engagement. We followed JBI's meta-aggregation approach for mixed methods reviews by qualitizing quantitative data through narrative interpretation and pooling to integrate the extracted data. RESULTS: A search of multiple databases yielded 5986 titles after de-duplication. Reviewers independently screened titles and abstracts and identified 511 citations for full text review, and 55 were included for data extraction. Thirty studies reported on cancer screening, most of which focused on sex-based cancers. There was significant variation in terminology used to describe TGD people. We observed a lack of breadth in data used among included studies, limiting the generalizability of results. Six studies engaged TGD people. Few studies investigated cancer outcomes or experiences during the diagnosis and survivorship phases; few focused on survival or mortality outcomes. CONCLUSION: We observed significant gaps in the body of research on TGD people and cancer. Efforts to improve the evidence base are needed to address knowledge gaps about TGD people's cancer experiences and outcomes and ensure the delivery of inclusive, evidence-based cancer care is possible.
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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 | 0.092 | 0.333 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.055 | 0.056 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".