Effect of testosterone formulations on hematocrit in transgender individuals: A systematic review
Bibliographic record
Abstract
BACKGROUND: Approximately, 11% of trans men experience erythrocytosis diagnosis due to testosterone administration during the first year of the gender-affirming hormone treatment (GAHT). OBJECTIVES: To identify and compare the effect of different testosterone formulations on hematocrit (Hct) and diagnose erythrocytosis in trans men. MATERIALS AND METHODS: This systematic review was based on PRISMA guidelines. We performed an electronic search of PubMed, Embase, and Web of Science in January 2024. The Newcastle-Ottawa scale was used to evaluate the quality of evidence in the observational studies. RESULTS: Of the 152 records retrieved, 18 met the eligibility criteria. Studies observed an increase of up to 5% in Hct in trans men using injectable testosterone undecanoate (TU), and up to 6.9% in trans men using intermediate injectable testosterone esters (TE). Trans men using TE experience a larger increase in serum Hct levels compared to those receiving TU. Erythrocytosis prevalence varies according to the cutoff used (50%, 52%, and 54%). Erythrocytosis was also associated with tobacco use, age at initiation of hormone therapy, body mass index (BMI), and pulmonary conditions. Studies that evaluated the effect of testosterone formulation on erythrocytosis diagnosis present conflicting result. Trans men have a hazard ratio of 7.4 (95% CI: 4.1, 13.4) of developing erythrocytosis compared to cisgender men, using a 52% hematocrit cutoff. CONCLUSION: All testosterone formulations result in an increase in Hct, irrespective of dose, formulation, and administration method. Smoking, higher age at initiation of the testosterone therapy, higher BMI, and a predisposing medical history are associated with this increase in Hct. The difference in effect of TE and TU on Hct is conflicting, although it is important to point out that these data come from observational studies, retrospective, and with a small-sample size.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".