Use of administrative claims data in observational studies of antirheumatic medication effects on pregnancy outcomes: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The primary objective of this review is to examine which disease-modifying antirheumatic drugs (DMARDs) and biologics used to treat pregnant individuals with rheumatic conditions have been reported in observational studies using population-based health administrative data. The secondary objective is to describe which adverse pregnancy outcomes (both maternal and neonatal) have been reported, their definitions, and corresponding diagnostic and/or procedural codes. INTRODUCTION: Pregnant individuals are typically excluded from drug trials due to unknown potential risks to both the pregnant person and fetus, leaving most antirheumatic drugs understudied for use in pregnancy. Despite these substantial knowledge gaps, most pregnant individuals continue to be maintained on antirheumatic medications due to the benefits generally outweighing the risks. In contrast to previous systematic reviews of findings from randomized trials, our scoping review aims to leverage this real-world data to generate real-world evidence of antirheumatic drug safety during pregnancy. INCLUSION CRITERIA: Articles must report on observational studies using population-based health administrative data from pregnant individuals with rheumatic conditions (rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis, and psoriatic arthritis) receiving antirheumatic drug therapy (DMARDs and biologics). Randomized trials, reviews, case studies, opinion pieces, and abstracts will be excluded. METHODS: Electronic databases (MEDLINE [Ovid], Embase [Ovid], CINAHL [EBSCOhost]) and gray literature (OpenGrey, Health Services Research Projects in Progress, World Health Organization Library, and Google Scholar) will be searched for relevant evidence. Search terms will combine 4 concepts: rheumatic diseases, drug therapy, pregnancy, and health care administrative data. Identified articles will be independently screened, selected, and extracted by 2 researchers. Data will be analyzed descriptively and presented in tables. REVIEW REGISTRATION: Open Science Framework https://osf.io/5e6tp.
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.
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 | 0.002 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".