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Record W2963771819 · doi:10.1002/pds.4794

Identifying pregnancies in insurance claims data: Methods and application to retinoid teratogenic surveillance

2019· article· en· W2963771819 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePharmacoepidemiology and Drug Safety · 2019
Typearticle
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Mental HealthCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsIsotretinoinMedicineDiscontinuationPregnancyMedical prescriptionObstetricsOdds ratioTretinoinTeratologyCohort studyRetinoidGynecologyGestationInternal medicineAcneRetinoic acidDermatology

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of the study is to develop an algorithm to identify pregnancies in administrative databases and apply it to assess pregnancy rates and outcomes in women prescribed isotretinoin or tretinoin. METHODS: Using the 2011 to 2015 Truven Health MarketScan Database, we identified pregnancies, including losses and terminations. In a cohort design, nonpregnant women filling a prescription for isotretinoin or tretinoin were matched to five women without either prescription. Women were followed for 365 days or until conception, medication discontinuation, or enrollment discontinuation ("prescription episode"). Rates of pregnancy, risks of pregnancy losses, and prevalence of infant malformations at birth were assessed by exposure. RESULTS: We identified 2 179 192 livebirths, 8434 stillbirths, 2521 mixed births, 415 110 spontaneous abortions, 124 556 elective terminations, and 8974 unspecified abortions. There were 86 834 isotretinoin and 973 587 tretinoin episodes, matched to 5 302 105 unexposed women. Pregnancy rates were 3 (isotretinoin), 19 (tretinoin), and 34 (unexposed) per 1000 person-years. Risk of spontaneous pregnancy losses were similar; however, terminations were more common in the isotretinoin-exposed (28% [95% CI: 21%-36%]) than the tretinoin-exposed (10% [95% CI: 9%-11%]) or unexposed pregnancies (6%). Malformations occurred in 4.5% (95% CI: 3.5%-5.6%) of the tretinoin-exposed pregnancies and 4.2% of the unexposed pregnancies (adjusted odds ratio: 1.16 [95% CI: 0.85-1.58]); isotretinoin-exposed births were too few to assess malformations. CONCLUSIONS: Administrative databases can complement risk evaluation and mitigation strategies (REMS) for known teratogens and contribute to safety surveillance for other medications. Here, isotretinoin-exposed pregnancy rates were low, but existent, and many pregnancies were terminated. Tretinoin exposure was not associated with a meaningfully elevated risk of losses or malformations as compared with unexposed pregnancies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.435
Teacher spread0.391 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it