Neural tube defects in native fars ethnicity in northern iran.
Bibliographic record
Abstract
BACKGROUND: Neural tube defects (NTD) are one of the leading causes of infant mortality worldwide. This study was designed to determine the prevalence of NTDs among native Fars ethnic groups during 1998-2005, and to identify maternal and demographic factors associated with NTDs. METHODS: We performed a descriptive cross-sectional hospital-based study in Dezyani Hospital, Gorgan, North of Iran, since January 1998 until December 2005. The design was based on a sample of 30,639 births of native Fars ethnic groups. Data were analyzed by using spss V13.5 software and were compared with the chi-square test. RESULTS: The prevalence of NTDs in Native Fars during the 8-year period was 25.4 per 10000 births (95% confidence interval: 20.1-31.8). The prevalence of NTDs was 20.6/10000 and 30.6/10000 in males and females respectively but this difference was not significant. The prevalence of spina bifida, anencephaly and encephalocele were 12.7, 11.4 and 1.3 per 10000 respectively. The rate of NTD was 48.9/10000 in newborns with mothers aged > 35 years. The highest rate of NTDs and spina bifida was in 2002. The highest and lowest rate of anencephaly was in 2005 and 2003 respectively. Twenty eight percent of the parents had consanguineous marriages. Degree relatedness 3, 4, 5 and 6 of consanguineous marriages were 12.8%, 9%, 3.8%, 2.5%, respectively. Also 47.5% of the parents resided in rural areas. CONCLUSION: This investigation showed that the rate of NTDs in Native Fars was higher in Iran. In addition, this rate is higher than the Canada and Ukraine and lower than Chinese people.
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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 | 0.001 | 0.000 |
| 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".