Analytic Methods for Evaluating Patterns of Multiple Congenital Anomalies in Birth Defect Registries
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: It is estimated that 20 to 30% of infants with birth defects have two or more birth defects. Among these infants with multiple congenital anomalies (MCA), co-occurring anomalies may represent either chance (i.e., unrelated etiologies) or pathogenically associated patterns of anomalies. While some MCA patterns have been recognized and described (e.g., known syndromes), others have not been identified or characterized. Elucidating these patterns may result in a better understanding of the etiologies of these MCAs. METHODS: This article reviews the literature with regard to analytic methods that have been used to evaluate patterns of MCAs, in particular those using birth defect registry data. RESULTS: A popular method for MCA assessment involves a comparison of the observed to expected ratio for a given combination of MCAs, or one of several modified versions of this comparison. Other methods include use of numerical taxonomy or other clustering techniques, multiple regression analysis, and log-linear analysis. Advantages and disadvantages of these approaches, as well as specific applications, were outlined. CONCLUSION: Despite the availability of multiple analytic approaches, relatively few MCA combinations have been assessed. The availability of large birth defects registries and computing resources that allow for automated, big data strategies for prioritizing MCA patterns may provide for new avenues for better understanding co-occurrence of birth defects. Thus, the selection of an analytic approach may depend on several considerations. Birth Defects Research 110:5-11, 2018. © 2017 Wiley Periodicals, Inc.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it