Sperm DNA integrity and sexual dysfunction among infertile men
Bibliographic record
Abstract
PURPOSE: To evaluate the association between sperm DNA fragmentation index (DFI), which quantifies the proportion of sperm with damaged DNA (sperm DNA fragmentation), and sexual dysfunction (SD) using the Sexual Health Inventory for Men (SHIM), a validated 5-item tool assessing erectile dysfunction severity, and the Androgen Deficiency in Aging Male (ADAM) questionnaire, a 10-item screening instrument for symptoms of testosterone deficiency. METHODS: A retrospective cohort study was conducted at a university-affiliated male infertility clinic. A total of 703 infertile men (mean age 37.4 ± 5.6 years) who completed SHIM and ADAM questionnaires and underwent semen analysis and DFI testing between 2000 and 2020 were included. DFI was categorized as normal (< 30%) or abnormal (≥ 30%). Primary outcomes were intercourse frequency (IF), SHIM scores (erectile dysfunction severity), and ADAM scores (androgen deficiency symptoms). Multivariable regression models evaluated predictors of sexual function, with emphasis on DFI. RESULTS: Abnormal DFI was observed in 39% of men. Average IF was 7.2 ± 4.4 times/month, with no difference by DFI status. A positive ADAM score was reported in 41.1%, while moderate/severe ED (SHIM) was reported in 3%. Multivariable analysis showed that BMI above 30 (kg/m²) alone was associated with reduced IF. Abnormal SHIM scores were predictive of positive ADAM score. Worse SHIM scores were associated with smoking and a positive ADAM score. Men with abnormal DFI had significantly lower SHIM scores (p = 0.02): 65% had normal scores versus 73% in the normal DFI group. Mild and mild-moderate ED were reported in 25% and 9% of the abnormal DFI group versus 19% and 5% in the normal group, respectively. CONCLUSION: Abnormal DFI was significantly associated with erectile dysfunction. These findings support incorporating sexual health assessments into male infertility evaluations.
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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 | 0.000 | 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) | 0.002 | 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".