Do pregnancy rates differ with intra-uterine insemination when different combinations of semen analysis parameters are abnormal?
Bibliographic record
Abstract
OBJECTIVE: To evaluate the relationship of one or a combination of semen analysis parameter results on insemination outcomes. MATERIAL AND METHODS: A retrospective analysis was performed to evaluate the effect on pregnancy rates in relation to one or more abnormal semen analysis parameters based on the 2010 World Health Organization semen analysis guidelines. RESULTS: Nine hundred eighty-one couples underwent 2231 intrauterine insemination cycles at the Stanford Fertility and Reproductive Medicine Center. In our study, the pregnancy rates ranged from 11-25% when an individual or combined semen analysis parameters were analyzed. Similar pregnancy rates were found when one, two, and in most cases three parameters were abnormal. When a single parameter was abnormal among volume, concentration, and motility, pregnancy rates were mainly unaffected. There was the exception of total sperm count where pregnancy rates were diminished when counts were below 39 million (p=0.04). CONCLUSIONS: Clearly, total sperm in the specimen and not the concentration of sperm per milliliter was the critical factor for predicting pregnancy. Therefore, a reorganization of semen analysis reports should be done emphasizing the total amount of sperm present and de-emphasizing concentration of sperm.
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 | 0.003 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".