A Gynecology-Led Transgender Clinic: Retrospective Review of Referral Patterns and Quality Indicators
Bibliographic record
Abstract
Abstract Background The Transgender Clinic at the Kingston Health Sciences Centre (KTC) was created in 2017 to meet the needs of transgender patients. Methods A retrospective review of all KTC patient charts was completed. The primary outcomes were the referral pattern to, and services provided by KTC. The secondary outcomes were the quality indicators i) rates of cervical screening and ii) offers of fertility preservation. Results 108 patients were referred and 92 were seen; the median age was 20 years old. 77.2% of all patients sought, and 72.8% ultimately received, hormone therapy. Cervical cancer screening was documented as up to date for 69.8% of eligible people. 81.7% of eligible patients had documented fertility preservation offers. Conclusion Accessible local transgender care is a standard that can and should be met in Canada and internationally. 77.2% of patients sought hormone therapy, which is in the scope of providers from many specialties Accessible local transgender care is a standard than can and should be met in Canada and internationally Gynecologists, pediatricians, and primary care providers are well-suited to provide transgender care
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".