Improving Data Collection in Pregnancy Safety Studies: Towards Standardisation of Data Elements in Pregnancy Reports from Public and Private Partners, A Contribution from the ConcePTION Project
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVE: The ConcePTION project aims to improve the way medication use during pregnancy is studied. This includes exploring the possibility of developing a distributed data processing and analysis infrastructure using a common data model that could form a foundational platform for future surveillance and research. A prerequisite would be that data from various data access providers (DAPs) can be harmonised according to an agreed set of standard rules concerning the structure and content of the data. To do so, a reference framework of core data elements (CDEs) recommended for primary data studies on drug safety during pregnancy was previously developed. The aim of this study was to assess the ability of several public and private DAPs using different primary data sources focusing on multiple sclerosis, as a pilot, to map their respective data variables and definitions with the CDE recommendations framework. METHODS: Four pregnancy registries (Gilenya, Novartis; Aubagio, Sanofi; the Organization of Teratology Information Specialists [OTIS]; Aubagio, Sanofi; the Dutch Pregnancy Drug Register, Lareb), two enhanced pharmacovigilance programmes (Gilenya PRIM, Novartis; MAPLE-MS, Merck Healthcare KGaA) and four Teratology Information Services (UK TIS, Jerusalem TIS, Zerifin TIS, Swiss TIS) participated in the study. The ConcePTION primary data source CDE includes 51 items covering administrative functions, the description of pregnancy, maternal medical history, maternal illnesses arising in pregnancy, delivery details, and pregnancy and infant outcomes. For each variable in the CDE, the DAPs identified whether their variables were: identical to the one mentioned in the CDE; derived; similar but with a divergent definition; or not available. RESULTS: The majority of the DAP data variables were either directly taken (85%, n = 305/357, range 73-94% between DAPs) or derived by combining different variables (12%, n = 42/357, range 0-24% between DAPs) to conform to the CDE variables and definitions. For very few of the DAP variables, alignment with the CDE items was not possible, either because of divergent definitions (1%, n = 3/357, range 0-2% between DAPs) or because the variables were not available (2%, n = 7/357, range 0-4% between DAPs). CONCLUSIONS: Data access providers participating in this study presented a very high proportion of variables matching the CDE items, indicating that alignment of definitions and harmonisation of data analysis by different stakeholders to accelerate and strengthen pregnancy pharmacovigilance safety data analyses could be feasible.
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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 | 0.565 | 0.517 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".