Frequency of holoprosencephaly in the International Clearinghouse Birth Defects Surveillance Systems: Searching for population variations
Bibliographic record
Abstract
BACKGROUND: Holoprosencephaly (HPE) is a developmental field defect of the brain that results in incomplete separation of the cerebral hemispheres that includes less severe phenotypes, such as arhinencephaly and single median maxillary central incisor. Information on the epidemiology of HPE is limited, both because few population-based studies have been reported, and because small studies must observe a greater number of years in order to accumulate sufficient numbers of births for a reliable estimate. METHODS: We collected data from 2000 through 2004 from 24 of the 46 Birth Defects Registry Members of the International Clearinghouse for Birth Defects Surveillance and Research. This study is based on more than 7 million births in various areas from North and South America, Europe, and Australia. RESULTS: A total of 963 HPE cases were registered, yielding an overall prevalence of 1.31 per 10,000 births. Because the estimate was heterogeneous, possible causes of variations among populations were analyzed: random variation, under-reporting and over-reporting bias, variation in proportion of termination of pregnancies among all registered cases and real differences among populations. CONCLUSIONS: The data do not suggest large differences in total prevalence of HPE among the studied populations that would be useful to generate etiological hypotheses.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".