Major Congenital Anomalies: A New Rising Tide of Concern to the Health System
Bibliographic record
Abstract
Introduction: WHO has considered Major Congenital Anomalies (MCA) as a recognizable cause of morbidity and mortality in infants and children under five years of age. Method: This is a descriptive study of antenatal MCA over 10 years period from January 2009 to December 2018. All data were analyzed statistically using STATA software (Stata Corporation, College Station, TX). Results: During the study period, there were 147563 patients. Of which, 1502 cases found to have major congenital anomalies, among them 947 (63.05 %) fetuses with isolated major anomalies and 555 cases (36.95%) with MCA. The average antenatal prevalence of MCA for 10 years was 10.1 per 1000 pregnancies. The mean gestational age during the first visit was 27(5.5) weeks with range from 10 to 40 weeks. The maternal age was 30 (6.0) years. Coexisting maternal factors were observed in 481 (32%) of patients including gestational diabetes (8.8%), maternal age (6.59%) and recurrent early pregnancy loss (7.12%). Nervous system was the most common (29%) abnormalities observed and cardiothoracic system (24.9%) was the second most common. Perinatal outcomes showed that 9.6 % had early neonatal death, 19% had still births and 4 % had neonatal death. The perinatal mortality rate was 32.6% among fetuses with major congenital anomalies. Conclusions: The prevalence of major congenital anomalies in our papulation is double the international figures. This study emphasizes the need of national surveillance system and database for congenital anomalies and efforts should be focused in rising awareness of the occurrence and risk factors of congenital anomalies.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".