Findings from the National Birth Defects Prevention Study: Interpretation and translation for the clinician
Bibliographic record
Abstract
BACKGROUND: The National Birth Defects Prevention Study (NBDPS) is a large U.S. multi-site case-control study first established in 1996 to identify potentially preventable environmental causes and genetic risk factors for more than 30 selected categories of major birth defects. METHODS: Numerous reports with both positive and negative findings have been produced by the NBDPS, and many have influenced clinical practice. Many NBDPS reports have included novel findings, and in some cases these findings could only be considered hypothesis-generating. Other reports have met criteria for causality such as replication of findings in other studies, biological plausibility, and coherence. RESULTS: However, translation of even strongly supported associations, in some cases, has required clinicians to learn to communicate information to patients about small and uncertain absolute risks in the context of the potential effects of under- or poorly treated maternal conditions. CONCLUSION: The NBDPS has continued to play an important role as a rich U.S. data source that can advance the understanding of maternal conditions and their treatments in relation to birth defects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.141 | 0.560 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".