OMOP CDM for breast cancer research: transforming the Breast Cancer Now Biobank data
Bibliographic record
Abstract
Abstract Common data models (CDMs) are essential for health data standardisation, which facilitates the governance and management of data, improves data quality and enhances the findability, accessibility, interoperability and reusability of data. They allow researchers to efficiently integrate health datasets and perform joint analysis on them, promoting collaboration and maximising translation of research outputs for patients’ benefit. We describe the process of transforming the biobank data for over 2,850 donors recruited at the Barts Cancer Institute (BCI) site of the Breast Cancer Now Biobank (BCNB) - the UK’s first national breast cancer biobank hosting longitudinal biospecimens and associated clinical, genomic and imaging data - into the Observational Medical Outcomes Partnership (OMOP) CDM. Our transformation pipeline achieved high coverage, with 83% of source concepts mapped, and our OMOP CDM achieved a total pass rate of 100% in quality assessments. We present the breast cancer characteristics of the resultant patient cohort. We report several challenges faced during the transformation process and explain how we addressed them, and discuss the strengths and limitations of adopting the OMOP CDM for breast cancer research. The OMOP-mapped BCNB-BCI dataset is a valuable resource that can now be explored and analysed alongside other health datasets.
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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 | 0.050 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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 source (direct Gemma or distilled Codex), 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".