The impact of semen processing on sperm parameters and pregnancy rates after intrauterine insemination
Bibliographic record
Abstract
BACKGROUND: The objective of this retrospective study was to evaluate the effect of semen processing on computer analyzed semen parameters and pregnancy rates after intrauterine insemination (IUI). METHODS: Over a two-year period, a total of 981 couples undergoing 2231 IUI cycles were evaluated and the freshly collected non-donor semen was analyzed before and after density gradient centrifugation (DGC). RESULTS: DGC led to significant increases in sperm concentration by 66±74 ×106/mL (P=0.0001), percentage of motile sperm by 24±22% (P=0.0001), concentration motile by 27±58 ×106/mL (P=0.0001), and forward sperm progression by 18±14 µ/s (P=0.0001). In 95% of cases, there was a decrease in the total motile sperm count (TMSC), with an average decrease of 50±124% compared to pre-processed samples (P=0.0001). Importantly, the decrease in TMSC did not negatively affect pregnancy rates (P=0.45). CONCLUSIONS: This study proves that DGC leads to significant increases in most sperm parameters, with the exception of TMSC. Remarkably, the decrease in TMSC did not affect the pregnancy rate. This should reassure clinicians when the TMSC is negatively affected by processing.
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 | 0.005 |
| 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) | 0.001 | 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".