Evaluation of Telomere Length in Spermatozoa as a Diagnostic Tool for Male Factor Infertility
Bibliographic record
Abstract
Over forty years since its inception, in vitro fertilization (IVF) has revolutionized the treatment of infertility (Edwards & Steptoe, 1980). The efficacy of IVF to treat severe male factor infertility, however, remains low due to the considerable knowledge gap in our understanding of its underlying etiology (Bernardini, 2000; Durak Aras et al., 2012; Tan et al., 1992). Clinical tests that hold predictive value towards pregnancy outcomes are still needed. In the last decade, the relationship between telomere length in sperm and male infertility has generated numerous confounding publications. In my research, I found that the effect size of sperm telomere length on fertility parameters (i.e., male age, sperm concentration, etc.) was relatively small; therefore, differences in study design moderator variables introduced sampling error and decreased the sensitivity of detection in previous publications. I also demonstrated that gold standard methods for telomere length determination were not applicable in sperm because the genome contained two distinct populations of telomeric DNA – telomeres on chromosome ends and free-floating extrachromosomal telomeric DNA repeats. I then applied the haloFISH assay, which measured the percentage of single sperm with truncated telomeres in individual patients, and confirmed that the presence of extrachromosomal telomeric DNA was a normal physiological characteristic of sperm, and that men who had shorter average sperm chromosomal telomere lengths had higher telomere length heterogeneity between sperm cells; an effect that is indicative of telomere crisis in somatic cells (Lansdorp, 1996; F. Wang et al., 2013). Given the preliminary sample size, the study was not adequately powered to detect a significant difference in chromosomal telomere length between groups, but a ‘medium’ effect size warrants further investigation to determine whether the length of telomeres in sperm can serve as a predictive clinical test for patients with male factor infertility.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".