Cocaine Use in the Infertile Male Population: A Marker for Conditions Resulting in Subfertility
Bibliographic record
Abstract
INTRODUCTION: We sought to evaluate the incidence and effect of cocaine use in the infertile male population. MATERIALS AND METHODS: Men presenting for fertility evaluation reporting cocaine usage were identified via prospectively collected database. Data were analyzed for usage patterns, reproductive history, associated drug use and medical conditions, hormonal and semen parameters. RESULTS: Thirty-eight out of 4,400 (0.9%) men reported cocaine use. Most used cocaine every 3 months or less. Compared with non-cocaine using men, cocaine users reported more recreational drug use (89 vs. 9.2%), marijuana use (78.9 vs. 11.4%), chlamydia (10.5 vs. 3%), herpes (7.9 vs. 2.5%), and tobacco use (55.3 vs. 19.5%). After excluding men with causes for azoospermia, the mean semen parameters for cocaine users were: volume 2.47 ± 1.02 ml; concentration 53.55 ± 84.04 × 10(6)/ml; motility 15.72 ± 12.26%; total motile sperm count 76.67 ± 180.30 × 10(6). CONCLUSIONS: Few (< 1%) men in our infertile population reported the use of cocaine, and the frequency of use was low. Given the low use rates and limitations of reporting bias, it is difficult to determine the direct effect of cocaine use on male fertility. However, while infrequent cocaine use seems to have limited impact on semen parameters, men reporting cocaine use represent a different cohort of men than the overall infertile population, with higher rates of concurrent substance abuse, tobacco use and infections, all of which may negatively impact their fertility. Reported cocaine users should be screened for concurrent drug use and infections.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".