Can We Rely on Pharmacy Claims Databases to Ascertain Maternal Use of Medications during Pregnancy?
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Administrative databases are increasingly used to measure drug exposure in perinatal pharmacoepidemiology. We aimed to estimate the concordance between records of prescriptions filled in pharmacies and self-reported drug use during pregnancy. METHODS: Data on self-reported medication use were collected at each trimester of pregnancy among a sub-sample from the Organization of Teratology Information Specialists Antidepressants in Pregnancy Cohort. Women were eligible if they were Quebec resident and provided their pharmacist's contact information. Maternal self-reports were compared with prescriptions filled in pharmacies, which are transferred to pharmaceutical services files of Quebec provincial health plan database (Régie de l'asssurance maladie du Québec). Positive and negative predictive values (PPV and NPV) for medications taken chronically (antidepressants, thyroid hormones), acutely (antibiotics), and as needed (antiemetics, asthma medications) were calculated. RESULTS: Among the 93 participants (mean age = 30.2 ± 3.8 years), 41.9% (n = 39) took at least one antidepressant during pregnancy according to self-reports, and 39.8% (n = 37) according to pharmacy records. Other commonly used drugs were antiemetics (self-reported 22.6%, pharmacy record 24.7%), antibiotics (20.4%, 16.1%), asthma medications (15.1%, 15.1%), and thyroid hormones (10.8%, 8.6%). PPVs and NPVs were: (1) chronic medication: antidepressants PPV = 100% (95% confidence interval [CI], 100-100%), NPV = 96% (95% CI, 92-100%); thyroid hormones PPV = 100% (95% CI, 100-100%), NPV = 98% (95% CI, 95-100%); (2) Acute medication: antibiotics PPV = 87% (95% CI, 70-100%), NPV = 92% (95% CI, 86-98%); (3) as needed medications: antiemetics: PPV = 78% (95% CI, 62-95%), NPV = 96% (95% CI, 91-100%); asthma: PPV = 33% (95% CI, 3-64%), NPV = 99% (95% CI, 97-100%). CONCLUSION: The high PPV and NPV validate the use of filled prescription data in large databases as a measure of medication exposure. Birth Defects Research 109:423-431, 2017. © 2017 Wiley Periodicals, Inc.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it