Breast Cancer Screening in Transgender Population: Review of literature
Bibliographic record
Abstract
This literature review explores breast cancer screening practices among transgender individuals globally, emphasizing the overlooked population in Pakistan. With an overview of intersex and transgender terminology, the study delves into screening guidelines for transfeminine and transmasculine patients, considering hormone therapy and surgery. Worldwide statistics on transgender and intersex populations are provided, highlighting the unique challenges they face, particularly in Pakistan, where societal discrimination and healthcare barriers persist. Databases searched included PubMed, Scopus, and Google Scholar from the Year 2000 till todate.The review synthesizes breast cancer screening recommendations in transgender population from ACR, WPATH, UCSF, and the Canadian Cancer Society, revealing variations in guidelines. It concludes with a call for tailored screening protocols for Pakistan’s transgender community and recommends a comprehensive study due to the absence of data in Southeast Asia. The unstructured abstract underscores the need for nuanced, personalized screening strategies and emphasizes the critical gap in knowledge specific to breast cancer in this marginalized population. doi: https://doi.org/10.12669/pjms.40.8.9132 How to cite this: Tareen S, Mooghal M, Shaikh K, Zeeshan S. Breast Cancer Screening in Transgender Population: Review of literature. Pak J Med Sci. 2024;40(8):1847-1852. doi: https://doi.org/10.12669/pjms.40.8.9132 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".