Longitudinal HIV care outcomes by gender identity in the United States
Bibliographic record
Abstract
Objective: Describe engagement in HIV care over time after initial engagement in HIV care, by gender identity.Design:Observational, clinical cohort study of people with HIV engaged in routine HIV care across the United States. Methods: We followed people with HIV who linked to and engaged in clinical care (attending ≥2 visits in 12 months) in cohorts in the North American Transgender Cohort Collaboration, 2000-2018. Within strata of gender identity, we estimated the 7-year (84-month) restricted mean time spent: lost-to-clinic (stratified by pre/postantiretroviral therapy (ART) initiation); in care prior to ART initiation; on ART but not virally suppressed; virally suppressed (≤200 copies/ml); or dead (pre/post-ART initiation). Results: Transgender women (N = 482/101 841) spent an average of 35.5 out of 84 months virally suppressed (this was 30.5 months for cisgender women and 34.4 months for cisgender men). After adjustment for age, race, ethnicity, history of injection drug use, cohort, and calendar year, transgender women were significantly less likely to die than cisgender people. Cisgender women spent more time in care not yet on ART, and less time on ART and virally suppressed, but were less likely to die compared with cisgender men. Other differences were not clinically meaningful. Conclusions: In this sample, transgender women and cisgender people spent similar amounts of time in care and virally suppressed. Additional efforts to improve retention in care and viral suppression are needed for all people with HIV, regardless of gender identity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".