Evaluation of a serum 17-hydroxyprogesterone as predictor of semen parameter(s) improvement in men undergoing medical treatment for infertility
Bibliographic record
Abstract
INTRODUCTION: The goal of medical therapy for infertile men with testosterone deficiency (TD) is to improve intratesticular testosterone (ITT). There is a gap in knowledge to identify those who will respond with semen parameter(s) improvement. We hypothesized that serum 17-hydroxyprogesterone (17-OHP) - a marker of ITT - can be used to predict improvement of semen parameter(s). METHODS: Between July 2018 and January 2020, we conducted a prospective study of 31 men with primary infertility, TD, and secondary hypogonadism receiving clomiphene citrate (CC) and/or human chorionic gonadotropin (hCG) for three months. We assessed baseline and followup hormones, including testosterone, 17-OHP, semen parameter(s), and demographics. Semen quality upgrading was based on assisted reproduction eligibility: in-vitro fertilization (<5 million), intrauterine insemination (IUI) (5-9 million), and natural pregnancy (>9 million). Variables were compared using the Mann-Whitney U or Wilcoxon rank test. RESULTS: Twenty-one men received CC and 10 received CC/hCG. Median followup was 3.7 (3.3-5.1) months. Sixteen men upgraded semen quality. Six of 10 men with baseline total motile sperm count (TMSC) of 0 had motile sperm after treatment, and 11/20 men with TMSC <5 upgraded semen quality into TMSC >5 range. Low 17-OHP was the only factor that predicted semen quality upgrading. Men with 17-OHP ≤55 ng/dL upgraded semen quality and improved hormones, whereas men with 17-OHP >55 ng/dL did not upgrade semen quality. CONCLUSIONS: Medical therapy for infertile men with TD resulted in the improvement of sperm concentration, TMSC, testosterone, and 17-OHP. Semen quality upgrading appears to be more significant in patients with low 17-OHP, suggesting that ITT can be used as a biomarker to predict semen parameter(s) improvement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".