Gender Inclusion in the Cytopathology Laboratory: Review of Current Practice and Call to Action
Bibliographic record
Abstract
Context.—: Ensuring equitable laboratory patient care within diverse populations is a priority. The cytopathology laboratory has an important role in providing gender-inclusive care, particularly with regard to screening and prevention of human papillomavirus-related carcinoma, for individuals who are transgender, gender nonbinary, intersex, and with same-gender sexual orientation. Providing equitable care necessitates an understanding of gender-inclusive processes within the cytopathology laboratory. Many barriers to implementation exist and include sociocultural, legal, ethical, and financial hurdles. Objective.—: To review the current literature regarding gender-inclusive care within a multi-institutional setting and identify challenges and opportunities for future growth in cytopathology. Specific areas of focus include appropriate terminology in laboratory information systems and requisitions, and variables affecting Papanicolaou test interpretation, human papillomavirus testing, and anal Papanicolaou test screening. Data Sources.—: Primary literature was searched within the areas highlighted throughout the article. Multi-institutional experiences from the authors, in addition to editorials and expert opinion, were used. Conclusions.—: The cytopathology laboratory has an important role in providing care that is inclusive and accurate for all patients. Gaps in care exist and further work is needed to address these disparities. This review attempts to increase awareness, educate, and share our own multi-institutional experiences, and calls for improvements in cytopathology to optimize quality in gender-inclusive patient care.
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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 | 0.020 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".