Genome-wide association studies in preterm birth: implications for the practicing obstetrician-gynaecologist
Bibliographic record
Abstract
Preterm birth has the highest mortality and morbidity of all pregnancy complications. The burden of preterm birth on public health worldwide is enormous, yet there are few effective means to prevent a preterm delivery. To date, much of its etiology is unexplained, but genetic predisposition is thought to play a major role. In the upcoming year, the international Preterm Birth Genome Project (PGP) consortium plans to publish a large genome wide association study in early preterm birth. Genome-wide association studies (GWAS) are designed to identify common genetic variants that influence health and disease. Despite the many challenges that are involved, GWAS can be an important discovery tool, revealing genetic variations that are associated with preterm birth. It is highly unlikely that findings of a GWAS can be directly translated into clinical practice in the short run. Nonetheless, it will help us to better understand the etiology of preterm birth and the GWAS results will generate new hypotheses for further research, thus enhancing our understanding of preterm birth and informing prevention efforts in the long run.
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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 | 0.021 | 0.078 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.022 | 0.033 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".