Accuracy of aspirin prophylaxis for preeclampsia prevention documentation within a large administrative dataset
Bibliographic record
Abstract
BACKGROUND: Low-dose aspirin prophylaxis is recommended for women at risk of preeclampsia. Capturing aspirin prophylaxis within administrative databases can be challenging since it is an over-the-counter medication. The Better Outcome Registry and Network (BORN) database, a perinatal health registry in Ontario, Canada, includes a formal variable that captures aspirin prophylaxis for preeclampsia. This variable has not been formally validated. OBJECTIVES: To assess the accuracy of the aspirin prophylaxis variable in the BORN database against an electronic medical record (EMR). METHODS: This validation study comprised 200 randomly selected women who had a livebirth at St. Michael's Hospital (SMH) in Toronto, Ontario, from January 2018 to July 2022. Recorded aspirin prophylaxis in pregnancy and maternal sociodemographic characteristics were independently extracted by two abstractors. Accuracy of aspirin prophylaxis use in the BORN database was compared to that in the SMH EMR, expressed as sensitivity, specificity, positive (PPV) and negative predictive values (NPV), Cohen's kappa (κ), and overall percent agreement, with 95% confidence intervals (CI). Sensitivity analyses were performed to account for missing or unclear aspirin prophylaxis use. RESULTS: Among 200 women, 24 (12.0%) received aspirin prophylaxis - 12.5% within the SMH EMR and 8.0% in the BORN database. Women using aspirin were older (37.0 vs 33.0 years) and had higher median gravidity (3 vs. 2). Sensitivity and specificity of the BORN aspirin prophylaxis variable were 62.5% (95% CI 40.6, 81.2) and 100.0% (95% CI 97.3, 100.0), respectively. The corresponding positive and negative predictive values were 100.0% (95% CI 78.2, 100.0), and 93.8% (95% CI 88.6, 97.1), respectively. Cohen's κ was 0.74 (95% CI 0.58, 0.90), and overall percent agreement was 94.4% (95% CI 87.1, 100.0). CONCLUSIONS: Aspirin use within the BORN database, based on a standard variable field, appears accurate enough for the potential use in epidemiological studies of aspirin prophylaxis for preeclampsia or as a covariate in related studies.
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How this classification was reachedexpand
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".