75 Internal Validity for Large Collaborative Clinical Datasets: Assessment of the CONGRESS Database
Bibliographic record
Abstract
Abstract Aim Multi-centre clinical research collaboratives generate large, generalisable datasets, but concerns remain regarding data quality due to variability in validation practices and the involvement of trainees with limited clinical or academic experience. This study aims to evaluate the feasibility, methodology, and effectiveness of internal data validation within the CONGRESS database, to inform best practices for ensuring data integrity in collaborative research settings. Method The multicentre CONGRESS dataset of early oesophago-gastric cancer was assessed. A random 20% sample of patients was selected to meet a >15% target validation size. Patient, disease and outcome data were re-abstracted from medical records and entered into a validation dataset which was compared to the original database. Cohen’s kappa coefficient (κ) and Pearsons corelation (r) were calculated to express the strength of agreement between categorical and continuous variables, respectively. Results In total, 302 patients (18.1%) from the original CONGRESS database were included in the validation dataset and 3320 data points were compared between datasets (6640 total). The percentage of exact agreement for variables ranged from 82.5-98.7% (median 92.3%, IQR 86.3-95.7%). 9 variables (1645/2946, 55.8% data points) showed “almost-perfect” agreement (κ or r >0.8), 5 (1301/2946, 44.2%) showed substantial agreement (κ > 0.6). None showed weak or poor agreement. Conclusions This study provides strong evidence of internal validity for the CONGRESS collaborative clinical database. It presents key learning points and a methodology for data validation, which can be applied to other large collaborative databases. This approach aims to enhance confidence in the quality and reliability of research conducted through these platforms.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".