Prevalence of Congenital Heart Disease: A Single Center Experience in Southwestern of Iran
Bibliographic record
Abstract
BACKGROUND: Congenital heart disease (CHD) refers to complex abnormalities that affect the structure or function of the heart due to embryonic defects. There is little accurate statistical data about prevalence, incidence and frequency in many developing countries such as Iran. The aim of this study was to evaluate the frequency of CHD in patients who were referred to the Department of Pediatric Cardiology in a large single-center in Southwestern of Iran. METHODS: This is a retrospective, cross-sectional study. Patients with various cardiac malformations were each investigated separately. A check list was used to collect information. It was comprised of three parts; demographic characteristics, Patient's birth details and maternal data. RESULTS: The frequency of ventricular septal defect (VSD), atrial septal defect (ASD) and tetralogy of fallot (TOF) were 125 (28.47%), 48 (10.93%) and 41(9.3%) respectively. Family history was reported in 26(11.1%) cases. Down syndrome, skeletal anomaly and hematological anomaly were the most common co-anomalies. Parental consanguinity was 48.7%. CONCLUSIONS: Present study showed that VSD was the most common CHD subtype followed by family history, familial marriage, extra cardiac anomalies (ECAs), birth weight, and maternal concomitant disease. But there was a controversial relationship between birth order and drug history in CHD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".