Abstract P018: Calibration of Analytes Over Twenty-Five Years in the Atherosclerosis Risk in Communities Study
Bibliographic record
Abstract
Background: Comparability of laboratory measures over time is important for studies of disease prevalence and progression. While a small amount of bias may seem negligible on an individual level, it can result in substantial misclassification of disease in the population. We conducted a calibration study of important biomarkers across five study visits (25 years) in ARIC. Methods: We re-measured 15 analytes in 200 blood samples to calibrate original measurements at each time point using Bland-Altman plots and Deming regression. We also assessed the impact of calibration on the prevalence of chronic kidney disease (CKD), defined by estimated glomerular filtration rate using creatinine (eGFRcr), and on trends over time. Results: Assays in samples frozen 12-27 years were highly correlated with original values (median r=0.95) after removing outliers (median 4% of values). The range of bias (% difference in means) across visits for each original analyte compared to its reference were: creatinine: 13-49%; uric acid: 3-24%; C-reactive protein: 3-9%; total cholesterol: 1-6%; high density lipoprotein cholesterol: 4-8% (but new methods differed); low density lipoprotein cholesterol: 1-5%; triglycerides: 2-4%; glucose: 1-4%; N-terminal prohormone of brain natriuretic peptide: 2-12%; high sensitivity cardiac troponin T: 1-9%; alanine transaminase (ALT): 21%; aspartate transaminase (AST): 17%; gamma glutamyl transpeptidase: 0.2%; ß2-microglobulin: 1%; beta-trace protein: 13%. Four analytes met calibration criteria: creatinine, uric acid, ALT and AST. The impact on CKD prevalence was substantial and similar to previous statistical calibration (22% uncalibrated, 1.9% previously and 1.3% current laboratory calibration). Trends in eGFRcr over time were better aligned after calibration ( Figure ). Conclusions: Repeat assay of samples shows high correlation with original values. Calibration enables application of absolute cutoffs (required for defining CKD and other conditions) and improves longitudinal analyses.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".