Age, body mass index, and number of previous trials: are they prognosticators of intra-uterine-insemination for infertility treatment?
Bibliographic record
Abstract
BACKGROUND: To examine whether pregnancy rate (PR) of intrauterine insemination (IUI) is related to certain demographic factors, such as age and body mass index (BMI), along with number of IUI cycles performed, a set of infertile Saudi women. MATERIALS AND METHODS: During this prospective study (a 24-month period), 301 Saudi women with infertility underwent IUI in our infertility clinic. We investigated whether PR is correlated with patient age and BMI, and the number of IUI trials, in order to determine if they could be used as prognosticators of pregnancy success. RESULTS: The highest PR was 14.89% for ages 19-25 and the lowest PR was 4.16% for ages 41-45, indicating no statistically significant difference among PR in all age groups (p value of 0.225). Also, in terms of BMI, the highest PR was 13.04% for BMI ≥35 and the lowest was 7.84% for BMI of <25 to 18.5, indicating no significant difference among different BMI groups (p value of 0.788). One-cycle treatment, as expected, was more successful (PR=12.84%) than 2-cycle treatment (PR=5.75%), however, 3-5-cycles treatment still showed encouraging results (PR=17.24%); but the difference did not reach statistical significance (p value=0.167). CONCLUSION: PR after IUI treatment remained approximately 10% from 19 to 40 years of age and declined after 40. Although no significant difference was observed among different age groups, earlier treatment is still recommended. There was a positive but not statistically significant correlation between PR and patient's BMI indicating that BMI is not a determining factor. There was also no correlation between PR and number of IUI trials. Patients can thus try as many times as they want before moving on to in vitro fertilization (IVF) treatment.
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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 | 0.001 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".