Laboratory Evaluation of Infertility-An Updated Review
Bibliographic record
Abstract
Background: Infertility is a significant global public health issue affecting approximately 15% of couples worldwide. Female fertility declines with advancing age due to progressive reduction in ovarian reserve and oocyte quality, while male, anatomical, endocrine, genetic, and environmental factors further contribute to reproductive failure. Accurate laboratory evaluation is central to identifying the underlying causes and guiding effective management. Aim: This review aims to provide an updated and comprehensive overview of the laboratory evaluation of infertility, emphasizing hormonal, semen, genetic, and biochemical assessments while highlighting methodological considerations and clinical relevance. Methods: A narrative review of current laboratory practices in infertility evaluation was conducted. The article synthesizes evidence on endocrine testing, ovarian reserve assessment, ovulatory function, semen analysis, genetic screening, immunoassay methodologies, and interfering factors affecting test accuracy. Results: Laboratory evaluation plays a pivotal role in infertility diagnosis, particularly through assessment of the hypothalamic–pituitary–ovarian axis, ovarian reserve markers (AMH, FSH, AFC), luteal progesterone levels, and comprehensive semen analysis. Immunoassays remain the mainstay of hormone testing, although interference from heterophilic antibodies, cross-reactivity, and preanalytical variables may compromise results. Advanced techniques such as LC–MS/MS improve analytical accuracy in selected cases. Genetic testing and quality control mechanisms further enhance diagnostic precision and clinical decision-making. Conclusion: An integrated laboratory approach, supported by rigorous quality control and awareness of assay limitations, is essential for accurate infertility evaluation. Tailored laboratory investigations enable personalized treatment strategies and improved reproductive outcomes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".