Methods, quality control and specimen management in an international multicentre investigation of type 1 diabetes: TEDDY
Bibliographic record
Abstract
BACKGROUND: The vast array and quantity of longitudinal samples collected in The Environmental Determinants of Diabetes in the Young study present a series of challenges in terms of quality control procedures and data validity. To address this, pilot studies have been conducted to standardize and enhance both biospecimen collection and sample obtainment in terms of autoantibody collection, stool sample preservation, RNA, biomarker stability, metabolic biomarkers and T-cell viability. RESEARCH DESIGN AND METHODS: The Environmental Determinants of Diabetes in the Young is a multicentre, international prospective study (n = 8677) designed to identify environmental triggers of type 1 diabetes (T1D) in genetically at-risk children from ages 3 months until 15 years. The study is conducted through six primary clinical centres located in four countries. RESULTS: As of May 2012, over three million biological samples and 250 million total data points have been collected, which will be analysed to assess autoimmunity status, presence of inflammatory biomarkers, genetic factors, exposure to infectious agents, dietary biomarkers and other potentially important environmental exposures in relation to autoimmunity and progression to T1D. CONCLUSIONS: Detailed procedures were utilized to standardize both data harmonization and management when handling a large quantity of longitudinal samples obtained from multiple locations. In addition, a description of the available specimens is provided that serve as an invaluable repository for the elucidation of determinants in T1D focusing on autoantibody concordance and harmonization, transglutaminase autoantibody, inflammatory biomarkers (T-cells), genetic proficiency testing, RNA lab internal quality control testing, infectious agents (monitoring cross-contamination, virus preservation and nasal swab collection validity) and HbA1c testing.
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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 | 0.148 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".