Cancer screening rates among transgender adults: Cross-sectional analysis of primary care data.
Bibliographic record
Abstract
OBJECTIVE: To compare rates of cervical, breast, and colorectal cancer screening between patients who are transgender and those who are cisgender (ie, nontransgender). DESIGN: Cross-sectional study. SETTING: A multisite academic family health team in Toronto, Ont, serving more than 45 000 enrolled patients. PARTICIPANTS: All patients enrolled in the family health team who were eligible for cervical, breast, or colorectal cancer screening. Patients were identified as transgender using an automated search of the practice electronic medical record followed by manual audit. MAIN OUTCOME MEASURES: tests, and logistic regression modeling was used to understand differences in screening after adjustment for age, neighbourhood income quintile, and number of primary care visits. RESULTS: = .046; adjusted OR = 0.50; 95% CI 0.26 to 0.99). CONCLUSION: In this setting, transgender patients were less likely to receive recommended cancer screening compared with the cisgender population. Future research and quality improvement activities should aim to understand and address potential patient, provider, and system factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".