Quality Control Measures over 30 Years in a Multicenter Clinical Study: Results from the Diabetes Control and Complications Trial / Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study
Bibliographic record
Abstract
Implementation of multicenter and/or longitudinal studies requires an effective quality assurance program to identify trends, data inconsistencies and process variability of results over time. The Diabetes Control and Complications Trial (DCCT) and the follow-up Epidemiology of Diabetes Interventions and Complications (EDIC) study represent over 30 years of data collection among a cohort of participants across 27 clinical centers. The quality assurance plan is overseen by the Data Coordinating Center and is implemented across the clinical centers and central reading units. Each central unit incorporates specific DCCT/EDIC quality monitoring activities into their routine quality assurance plan. The results are reviewed by a data quality assurance committee whose function is to identify variances in quality that may impact study results from the central units as well as within and across clinical centers, and to recommend implementation of corrective procedures when necessary. Over the 30-year period, changes to the methods, equipment, or clinical procedures have been required to keep procedures current and ensure continued collection of scientifically valid and clinically relevant results. Pilot testing to compare historic processes with contemporary alternatives is performed and comparability is validated prior to incorporation of new procedures into the study. Details of the quality assurance plan across and within the clinical and central reading units are described, and quality outcomes for core measures analyzed by the central reading units (e.g. biochemical samples, fundus photographs, ECGs) are presented.
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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 | 0.117 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".