Systematic review of national guidelines on cancer prevention in trans and gender-diverse populations
Bibliographic record
Abstract
Abstract Transgender and gender-diverse (TGD) individuals face barriers to cancer screening and HPV vaccination, as many programs rely on legal sex, and systemic issues such non-inclusive records and lack of provider training persist, leading to low uptake. Inclusive guidelines are essential to ensure equitable cancer prevention, but their global availability remains limited. This systematic review aimed to identify and describe national guidelines on cancer prevention addressing TGD populations. The search was conducted in Sept. 2024 using Overton, supplemented by citation mining and manual searches. Documents were classified by service and document type (official guideline vs recommendation). Thirty-seven documents from 11 countries were identified. For breast cancer screening, 9 guidelines from Australia, Canada, US, UK, Ireland, Netherlands and 7 recommendations, from these countries and Italy, were retrieved. For cervical cancer screening 6 guidelines (Australia, Canada, US, UK, Ireland, Netherlands) and 9 recommendations, including from Italy, Belgium and France were identified. Colorectal cancer screening was addressed by the UK and Ireland, but specific TGD adaptations were limited. For anal cancer, 1 Italian society statement addressed high-risk groups. For prostate cancer, 3 recommendation from the US, UK and Ireland were identified. Over 47 countries have implemented gender-neutral HPV vaccination policies, indirectly covering many TGD individuals. Seven guidelines (Canada, US, UK, Sweden), and 6 recommendations including from Italy and Spain, addressed TGD populations specifically. Across all services, only few documents from Canada, the UK, and Ireland provided detailed operational instructions for eligibility, invitations and procedures. Despite growing awareness, most cancer prevention policies inadequately address the needs of TGD populations. Anatomy-based screening pathways and standardized national guidelines are urgently needed to achieve equitable access. Key messages • Few countries have issued cancer screening or HPV vaccination guidelines fully inclusive of transgender and gender-diverse individuals, with major gaps across all services. • Standardized, anatomy-based eligibility and clear invitation protocols are urgently needed to improve equitable cancer prevention for transgender and gender-diverse populations worldwide.
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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 | 0.017 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".