Breast cancer risk and breast screening for trans people: an integration of 3 systematic reviews
Bibliographic record
Abstract
BACKGROUND: Trans people face uncertain risk for breast cancer and barriers to accessing breast screening. Our objectives were to identify and synthesize primary research evidence on the effect of cross-sex hormones (CSHs) on breast cancer risk, prognosis and mortality among trans people, the benefits and harms of breast screening in this population, and existing clinical practice recommendations on breast screening for trans people. METHODS: We conducted 2 systematic reviews of primary research, 1 on the effect of CSHs on breast cancer risk, prognosis and mortality, and the other on the benefits and harms of breast screening, and a third systematic review of guidelines on existing screening recommendations for trans people. We searched PubMed, MEDLINE, Embase, CINAHL, the Cochrane Database of Systematic Reviews and grey literature sources for primary research, guidelines and position statements published in English between 1997 and 2017. Citations were screened by 2 independent reviewers. One reviewer extracted data and assessed methodological quality of included articles; a second reviewer verified these in full. The results were synthesized narratively. RESULTS: Four observational studies, 6 guidelines and 5 position statements were included. Observational evidence of very low certainty did not show an effect of CSHs on breast cancer risk in trans men or trans women. Among trans women, painfulness of mammography and ultrasonography was low. There was no evidence on the effect of CSHs on breast cancer prognosis and mortality, or on benefits and other harms of screening. Existing clinical practice documents recommended screening for distinct trans subpopulations; however, recommendations varied. INTERPRETATION: The limited evidence does not show an effect of CSHs on breast cancer risk. Although there is insufficient evidence to determine the potential benefits and harms of breast screening, existing clinical practice documents generally recommend screening for trans people; further large-scale prospective comparative research is needed.
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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 | 0.053 | 0.179 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.023 |
| Bibliometrics | 0.036 | 0.035 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| 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".