Sperm Morphology Assessment in the Era of Intracytoplasmic Sperm Injection: Reliable Results Require Focus on Standardization, Quality Control, and Training
Bibliographic record
Abstract
Semen analysis is the first, and frequently, the only step in the evaluation of male fertility. Although the laboratory procedures are conducted according to the World Health Organization (WHO) guidelines, semen analysis and especially sperm morphology assessment is very difficult to standardize and obtain reproducible results. This is mainly due to the highly subjective nature of their evaluation. ICSI is the choice of treatment when sperm morphology is severely abnormal (teratozoospermic). Hence, the standardization of laboratory protocols for sperm morphology evaluation represents a fundamental step to ensure reliable, accurate and consistent laboratory results that avoid misdiagnoses and inadequate treatment of the infertile patient. This article aims to promote standardized laboratory procedures for an accurate evaluation of sperm morphology, including the establishment of quality control and quality assurance policies. Additionally, the clinical importance of sperm morphology results in assisted reproductive outcomes is discussed, along with the clinical management of teratozoospermic patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".