Ethnicity is important for creatinine excretion among Inuit and Caucasians in Greenland
Bibliographic record
Abstract
BACKGROUND: Human nutrition, contamination and renal function are commonly assessed by the analysis of urine. A complete 24-hour urine sample is the ideal but it is inconvenient and unreliable. Thus, spot urine sampling with creatinine adjustment is widely used. Stratification for age and gender is recommended. Still, ethnicity may influence creatinine excretion. METHODS: We collected 104 24-h urine samples among Inuit and non-Inuit living in Greenland. Completeness of sampling was checked by using para-amino benzoic acid (PABA) that also allowed for compensation of creatinine excretion when sampling was incomplete. We measured creatinine using the Jaffe method and PABA by the HPLC method. RESULTS: Participants were recruited from the capital city, a major town and a settlement (n = 36/48/20). They were aged 30-69 years with 78 Inuit and 26 non-Inuit. Inuit were smaller than non-Inuit (Caucasians): height, 163 vs. 177 cm, p < 0.001; weight, 71 vs. 84 kg, p = 0.001 with similar BMI. Creatinine excretion was lower in Inuit compared to non-Inuit (men, 1344/1807 mg/24 h; women 894/1259 mg/24 h; p = 0.002; 0.02). It was influenced by age (p < 0.001), gender (p < 0.001), weight (p = 0.001) and ethnicity (p = 0.030) while not by the intake of the protein-rich Inuit diet in the adjusted analysis. Creatinine excretion was described by: Inuit men, 1925 mg - (13.1 × age); Inuit women, 1701 mg - (17.0 × age). CONCLUSION: Inuit and Caucasians have different creatinine excretion. It is recommended to stratify by ethnicity in addition to adjustment for age and gender when using creatinine correction of spot urine samples.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".