Assessment of recording bias in pregnancy studies using health care databases: An application to neurologic conditions
Bibliographic record
Abstract
BACKGROUND: Pre-existing conditions are imperfectly recorded in health care databases. We assessed whether pre-existing neurologic conditions (epilepsy, multiple sclerosis [MS]) were differentially recorded in the presence of major obstetric outcomes (Caesarean delivery, preterm delivery, preeclampsia) in delivery records. We also evaluated the impact of differential recording on measures of frequency and association between the conditions and outcomes. METHODS: The 2011-2014 Truven Health MarketScan® Commercial Claims Dataset was used to identify pregnancies. We calculated the relative recording of epilepsy and MS at delivery hospitalization compared with a 270-day pre-delivery window both overall and by the presence of major obstetric outcomes. We estimated risk ratios for the association between epilepsy and MS with the outcomes for each ascertainment window. RESULTS: We identified 909 065 pregnancies in women continuously enrolled from 270-days before the delivery date. Of women with epilepsy identified in the pre-delivery window, 73% had the condition coded at delivery. For MS, the proportion was 60%. MS recording at delivery did not vary by obstetric outcomes, however, delivery-coded epilepsy was less likely confirmed in the pre-delivery window in the presence of preeclampsia. Generally, the period of ascertainment did not meaningfully impact risk ratios, however, the risk ratio for preeclampsia associated with epilepsy was 1.67 (95% CI 1.47, 1.90) when epilepsy was ascertained at delivery and 1.26 (95% CI 1.07, 1.48) when epilepsy was ascertained in the pre-delivery window (heterogeneity, P = .007). CONCLUSIONS: Ascertainment of epilepsy and MS in delivery hospitalization records underestimated prevalence. However, the window of recording generally did not impact risk ratio estimates of associations with obstetric outcomes.
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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 | 0.516 | 0.770 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.012 | 0.026 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".