How to Design Electronic Case Report Form (eCRF) Questions to Maximize Semantic Interoperability in Clinical Research
Bibliographic record
Abstract
Unlabelled: Case report forms (CRFs) are the instruments used by research organizations worldwide to collect information about patients and study participants with the purpose of answering specific questions, assessing the efficacy and safety of medical products, and in general improving prevention and treatment in health care. To obtain significant research results out of the collected data, CRFs should be designed following the recommendations issued by regulatory authorities. However, we believe that semantic interoperability in CRFs has not yet been properly addressed. Within an international consortium comprising several COVID-19 cohorts, we scrutinized the questions included in the different CRFs with the purpose of establishing semantic interoperability across the different study data elements so that data could be merged and jointly analyzed. We realized that similar concepts were structured very differently across the different CRFs, making it hard to find and match the information. Based on the experience acquired, we developed 5 guiding principles on how to design CRFs to support semantic interoperability and increase data quality while also facilitating the sharing of data. Our aim in this viewpoint is to provide general suggestions that, in our opinion, should support researchers in designing CRFs. We conclude by urging authorities to establish an international coordination board for standards and interoperable clinical study data with competence in clinical data, interoperability standards, and data protection as part of a preparedness plan for future pandemics or other health threats.
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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 | 0.287 | 0.435 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".