Within‐individual and within‐group correspondence of creatinine‐normalized urinary biomarkers of in vivo oxidation: TBARS and 8‐OHdG
Bibliographic record
Abstract
Background: Urinary biomarkers, such as TBARS or 8‐hydroxy‐d‐guanosine (8‐OHdG), are used in assessment of severe, clinical oxidative stress, but little is known about their diagnostic value within the presumptive normative range. Objective: To assess the association between 2 urinary biomarkers of in vivo oxidation (TBARS and 8‐OHdG) in subjects undergoing a supplemental iron‐induced oxidation challenge. Methods: Five adult males received two 7‐d courses of: A=120 mg Fe (FeSO 4 ) and B=120 mg Fe + 5 mL vegetable oil. Creatinine‐normalized, urinary TBARS and 8‐OHdG concentrations were quantified in 22 samples from baseline, active supplementation, and washout phases. Results: A significant within‐individual correlation between the urinary biomarkers was observed (Spearman r=0.465, p<0.01, n=110); kappa coefficients for the correspondence of tandem interval increments or decrements of the biomarkers concentrations ranged from 0.24 – 0.81 at individuals’ and group levels, with 4 of 6 between 0.40–0.81 (fair to very good). Conclusions: Within the normative range, we find intriguing within‐individual and within‐group correspondence between the two urinary biomarkers of in vivo oxidation. Sponsored in part by the Malaysian Palm Oil Board and INF‐EMF.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".