Incidence and Types of Congenital Anomalies in Newborns in Sulaimaniyah City in Iraq
Bibliographic record
Abstract
Congenital anomalies or birth defects can be acquired during the fetal stages of development or from the genetic makeup of the parents. Congenital anomalies are important causes of infant and childhood illness and disability. Little is known about incidence and types of these anomalies in Iraqi Kurdistan. Therefore, this study was undertaken to estimate the incidence and types of congenital anomalies in Sulaimaniyah city. The study was carried out on the hospital's records of all newborns registered as having a congenital anomaly. The records of 586 neonates with congenital anomalies were analyzed from a total of 178,954 live broths that occurred during 4 years in the city. The data was obtained from the statistics section of maternal and a child unit of the Preventive Health Department. The overall incidence of all types of congenital anomalies over the four years was 3.3/1000 live births. There was a statistically significant difference in incidence between males and females over the four years, male to female risk ratio 1.2 (95% CI 1.02-1.42, P= 0.03). The commonest congenital anomalies affected the cardiovascular system accounting for 24% followed by those of the nervous system with 16%. Down syndrome accounted for 14% of all anomalies and cleft lip/palate for 11%. Types of anomalies were statistically associated with low birth weight and maternal age. The study indicates that the incidence of congenital anomalies is not high in the region; however, more extensive studies are required to give a more realistic incidence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".