Social Determinants of Health Are Associated with Markers of Renal Injury in Adolescents with Type 1 Diabetes
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between the social determinants of health and markers of early renal injury in adolescent patients with type 1 diabetes (T1D). STUDY DESIGN: Renal outcomes included estimated glomerular filtration rate (eGFR) and albumin-creatinine excretion ratio (ACR). Differences in urinary and serum inflammatory markers also were assessed in relation to social determinants of health. Regression analysis was used to evaluate the association between the Ontario Marginalization Index (ON-Marg) as a measure of the social determinants of health, patient characteristics, ACR, eGFR, and renal filtration status (hyperfiltration vs normofiltration). RESULTS: . Increasing marginalization was positively associated with eGFR (P < .0001) but not with ACR (P = .605). Greater marginalization was associated with greater median levels of urinary interleukin (IL)-2, IL-12 (p40), macrophage-derived chemokine, monocyte chemoattractant protein-3, and tumor necrosis factor-β and serum IL-2. ON-Marg was significantly associated with eGFR after we controlled for age, sex, body mass index z score, ethnicity, serum glucose, and hemoglobin A1c in linear regression. A similar association between hyperfiltration and ON-Marg score was observed in multivariable logistic regression. CONCLUSION: Increasing marginalization is significantly associated with both eGFR and hyperfiltration in adolescents with T1D and is associated with significant changes in urinary inflammatory biomarkers. These findings highlight a potentially important interaction between social and biological determinants of health in adolescents with T1D.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".