Biochemical markers for prediction of the first half pregnancy losses: a review
Bibliographic record
Abstract
Objective: 26% of all pregnancies end in miscarriage, and up to 10% of clinically diagnosed pregnancies, and recurrent pregnancy loss is 5% among couples of childbearing ages. Although there are several known causes of pregnancy loss in the first half, including recurrent pregnancy loss, including parental chromosomal abnormalities, uterine malformations, endocrinological disorders, and immunological abnormalities, about half of the cases of pregnancy loss in its first half remain unexplained. Methods: The review includes observational controlled studies (case-control or cohort, longitudinal studies, reviews, meta-analyses), which include the study of biochemical factors for predicting pregnancy losses in the first half, in singlet pregnancy. The Newcastle-Ottawa Scale (NOS) was used to assess the research quality. Results: Finally, 27 studies were included in the review, which has 134904 examined patients. The results of the review include estimates of β-human chorionic gonadotropin, progesterone, pregnancy-associated protein - A, angiogenic vascular factors, estradiol, α-fetoprotein, homocysteine and CA-125 as a predictors or markers of the first half pregnancy losses. Conclusion: It may be concluded that to date, research data indicate the unavailability of any reliable biochemical marker for predicting pregnancy losses in its first half and require either a combination of them or comparison with clinical evidence. A fairly new model shall be considered for the assessment of α-fetoprotein in vaginal blood, which may have great prospects in predicting spontaneous miscarriages.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".