Development and validation of an algorithm to predict stillbirth gestational age in Medicaid billing records
Bibliographic record
Abstract
With Medicaid covering half of US pregnancies, Medicaid Analytic eXtract (MAX) provides a valuable data source to enrich understanding about stillbirth etiologies. We developed and validated a claims-based algorithm to predict gestational age (GA) at stillbirth. We linked the stillbirths identified in MAX 1999-2013 to Florida fetal death records (FDRs) to obtain clinical estimates of GA (n = 825). We tested several algorithms, including using a fixed median GA, median GA at the time of specific prenatal screening tests, and expanded versions considering additional predictors of stillbirth, including linear regression and random forest models. We estimated the proportion of pregnancies with differences of ±1, 2, 3 and 4 weeks between the predicted and FDR GA and the model mean square error (MSE). We validated the selected algorithms in 2 external samples. The best performing algorithm was a random forest model (MSE, 12.67 weeks2) with 84% of GAs within ±4 weeks. Assigning a fixed GA of 28 weeks resulted in an MSE of 60.21 weeks2 and proportions of GA within ±4 weeks of 32%. We observed consistent results in the external samples. Our prediction algorithm for stillbirths can facilitate pregnancy research in the Medicaid population.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".