Genetic causes of early miscarriage in patients after assisted reproductive technologies
Bibliographic record
Abstract
Objective. To analyze the frequency of occurrence of a pathological karyotype during pregnancy that occurred naturally and through the use of ART. Miscarriage (MC) is a significant problem for the medical community. There is a number of factors affecting the process of gestation. Currently, there is an opinion about the impact of assisted reproductive technologies (ART) methods on the fetal karyotype and an increase in the risk for MC when they are used.
 Materials and methods. The study used the data of karyotyping of abortive material from 256 women diagnosed the non-developing pregnancy with indication of age and gestational age at the time of termination of pregnancy for the period from 2018 to 2020, provided by JSC "Center for Family Medicine" in Yekaterinburg. In the course of the work, a statistical analysis of the frequency of occurrence of pathological karyotypes in different groups, identified on the basis of the method of pregnancy, was carried out.
 Results. To a greater extent, the problem of MC associated with a pathological fetal karyotype, occurs during natural pregnancy. This may be due to the lack of pregravid preparation. In addition, during ART, especially with the use of donor material, the cells without karyotype abnormalities are selected. The problem of MC after the use of ART may be associated with other somatic or functional risk factors.
 Conclusions. The available data on the possible genetic causes of early MC draw attention to the need for preimplantation genetic testing to make a timely diagnosis of fetal chromosomal abnormalities. In order to improve the reproductive health of the population, such method as a pregnant woman genetic passport can be proposed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 teacher head, 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".