Risk of kidney disease following a pregnancy complicated by diabetes: a longitudinal, population-based data-linkage study among Aboriginal women in the Northern Territory, Australia
Bibliographic record
Abstract
AIMS/HYPOTHESIS: The aim of this work was to investigate the risk of developing chronic kidney disease (CKD) or end-stage kidney disease (ESKD) following a pregnancy complicated by gestational diabetes mellitus (GDM) or pre-existing diabetes among Aboriginal women in the Northern Territory (NT), Australia. METHODS: We undertook a longitudinal study of linked healthcare datasets. All Aboriginal women who gave birth between 2000 and 2016 were eligible for inclusion. Diabetes status in the index pregnancy was as recorded in the NT Perinatal Data Collection. Outcomes included any stage of CKD and ESKD as defined by ICD-10 coding in the NT Hospital Inpatient Activity dataset between 2000 and 2018. Risk was compared using Cox proportional hazards regression. RESULTS: Among 10,508 Aboriginal women, the mean age was 23.1 (SD 6.1) years; 731 (7.0%) had GDM and 239 (2.3%) had pre-existing diabetes in pregnancy. Median follow-up was 12.1 years. Compared with women with no diabetes during pregnancy, women with GDM had increased risk of CKD (9.2% vs 2.2%, adjusted HR 5.2 [95% CI 3.9, 7.1]) and ESKD (2.4% vs 0.4%, adjusted HR 10.8 [95% CI 5.6, 20.8]). Among women with pre-existing diabetes in pregnancy, 29.1% developed CKD (adjusted HR 10.9 [95% CI 7.7, 15.4]) and 9.9% developed ESKD (adjusted HR 28.0 [95% CI 13.4, 58.6]). CONCLUSIONS/INTERPRETATION: Aboriginal women in the NT with GDM or pre-existing diabetes during pregnancy are at high risk of developing CKD and ESKD. Pregnancy presents an important opportunity to identify kidney disease risk. Strategies to prevent kidney disease and address the social determinants of health are needed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".