Bibliographic record
Abstract
Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications which affect the mother and offspring. In addition to adverse perinatal outcomes, it may lead to permanent health problems for the mother, such as type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD), while increasing the risk of future obesity, CVD, T2DM and GDM in the child. Approximately 15% of women seek fertility treatment. Over the last decade, it has come to attention that patients with an infertility history are more prone to having GDM during their pregnancies, and this review examines the relationship between GDM and infertility. The elevated estrogen, progesterone, leptin, placental lactogen and growth hormone are the main reasons for increased insulin resistance during pregnancy. Despite some confounding factors in the mechanism of GDM in patients with an infertility history, infertility treatment increases the risk, according to numerous studies. The obesity epidemic and associated disorders have become a significant public health concern worldwide. Lifestyle modification for weight loss before pregnancy is encouraged, but there is no strong evidence for improvement in perinatal results. GDM, infertility and infertility treatment have a potential risk of alteration in the embryo’s environment and cause epigenetic reprogramming, which may be inherited to the next generation. The fertility treatment impacts the patient’s and offspring’s health. Patients should be informed about the risks so that they consent and get involved in the decision. Infertility treatment may be accepted as a reason for high-risk pregnancy, and patients can be screened for GDM in early pregnancy.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".