A Pragmatic Method to Integrate Data From Preexisting Cohort Studies Using the Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model: Case Study
Bibliographic record
Abstract
BACKGROUND: In recent years, many researchers have focused on legacy data utilization, such as pooled analyses that collect and re-analyze data from multiple studies. However, the methodology for the integration of pre-existing databases whose data were collected for different purposes has not been established. Previously, we developed a tool to efficiently generate Study Data Tabulation Model (SDTM) data from hypothetical clinical trial data using the Clinical Data Interchange Standards Consortium (CDISC) SDTM. OBJECTIVE: To design a practical model for integrating pre-existing databases using the CDISC SDTM. METHODS: Data integration was performed in three phases: i) confirmation of the variables, ii) SDTM mapping, and iii) generation of the SDTM data. In phase 1, the definitions of the variables in detail were confirmed, and the datasets were converted to vertical datasets. In phase 2, the items derived from the SDTM format were set as mapping items. Three types of metadata (domain name, variable name, and test code), based on the CDISC SDTM, were embedded in the REDCap field annotation. In phase 3, the data dictionary, including the SDTM metadata, were output in the Operational Data Model (ODM) format. Finally, the mapped SDTM were generated using REDCap2SDTM v2. RESULTS: SDTM data were generated as a comma-separated values file for each of the seven domains defined in the metadata. Twenty-two items were commonly mapped to three databases. Because the SDTM data were set in each database correctly, we were able to integrate three independently pre-existing databases into one database in the CDISC SDTM format. CONCLUSIONS: Our project suggests that the CDISC SDTM is useful for integrating multiple pre-existing databases.
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 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 | 0.086 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".