Cumulative delivery rates in different age groups after artificial insemination with donor sperm
Bibliographic record
Abstract
BACKGROUND: Although the age-effect on in vitro fertilization outcomes has been well documented, data on donor insemination are scarce hampering accurate patient counseling. This cohort study therefore aims at analyzing cumulative delivery rates after donor insemination for various indications. METHODS: A large retrospective analysis was performed on 6630 insemination cycles in 1654 women. Delivery rates were calculated by life-table analysis after a maximum of 12 cycles in five subgroups of age when starting inseminations. Multivariable modeling was used to explore the effects according to age, indication (male infertility, lesbian couple or single-parent request) and ovarian stimulation protocol (none, clomiphene citrate or gonadotrophins). RESULTS: Overall, 928 deliveries were observed, i.e. a delivery rate of 14% per cycle and an expected cumulative delivery of 77% after 12 cycles. Subgroup analysis showed an expected cumulative delivery after 12 cycles of 87% for the group aged 20-29, 77% for ages 30-34, 76% for ages 35-37, 66% for ages 38-39 and 52% for ages 40-45. Drop-out analysis in the latter subgroup showed that only one patient discontinued treatment because of medical reasons. In contrast to age, neither indication nor ovarian stimulation protocol had any significant effect on the delivery rate. CONCLUSIONS: Our study corroborates the impact of age on donor insemination outcome. Nevertheless, even in some older age subgroups, acceptable expected cumulative delivery rates were observed. Despite this, the main reason for discontinuing treatment, however, was the anticipated low success rate. Women, up until 42 years of age, could be encouraged to continue 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".