Epidemiology of Major Congenital Malformations with Specific Focus on Teratogens
Bibliographic record
Abstract
BACKGROUND: Major congenital malformations (MCMs) are a significant cause of infant morbidity and mortality and constitute an important societal and economic burden. METHODS: We conducted a literature review to synthesize current evidence on MCMs. Specific objectives were to: 1) summarize internationally reported prevalence of MCMs based on registries and surveillance systems; 2) describe the epidemiology of different MCM types including critical periods and causative factors; 3) to identify the role played by principal known teratogens on the increase in the risk of MCMs; and 4) determine challenges associated with the epidemiologic assessment of potential risk factors for MCMs as well as potential preventive measures. RESULTS: It is estimated that 7.9 million infants worldwide are born every year with a MCM, yet there is considerable variation in reported rates across countries. This may be attributable to varying definitions arising from heterogeneity among different classes with respect to critical periods for embryogenesis and organogenesis. There is also substantial etiologic heterogeneity among MCMs classes that potentially contribute to challenges in epidemiologic studies. Modifiable factors such as pharmacologic exposures have received considerable attention and a number of drugs have been shown to be teratogenic including folic acid antagonists, angiotensin converting enzyme inhibitors, antidepressants, anticonvulsants, coumarin derivatives and retinoids including isotretinoin. CONCLUSION: The majority of MCMs are due to unexplained causes. Other contributing factors include genetics, environmental factors, multifactorial inheritance, maternal-related conditions, and maternal drug or chemical exposure. However, there remains a need to better understand the epidemiology of MCMs when studying drug effect during gestation.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".