Association Between Blunted Glomerular Hyperfiltration in Pregnancy and Severe Maternal Morbidity—A Research Letter
Bibliographic record
Abstract
BACKGROUND: Glomerular hyperfiltration is one physiological adaptation to pregnancy, marked by a decline in serum creatinine (SCr) concentration by 16 weeks' gestation. It is not known whether blunted glomerular hyperfiltration leads to adverse maternal outcomes, including severe maternal morbidity (SMM). OBJECTIVE: To evaluate the association between blunted glomerular hyperfiltration and subsequent SMM or death. DESIGN: Population-based cohort study. SETTING: Ontario, Canada, from 2008 to 2019. PARTICIPANTS: weeks' gestation ("in-pregnancy"). Excluded were women who died before birth, who had end-stage renal disease or kidney transplantation before conception, or whose pre-pregnancy SCr was 125 μmol/L. EXPOSURE: Net glomerular hyperfiltration defined as the preconception minus the in-pregnancy SCr. MEASURES: The primary study outcome was SMM or death arising from 23 weeks' gestation up to 42 days after the index birth. METHODS: Adjusted relative risks (aRRs) were calculated using Modified Poisson regression per 1-SD net blunting of glomerular hyperfiltration adjusting for important covariates. RESULTS: A total of 10,323 births met all inclusion criteria. The mean (SD) SCr was 61.7 (11.0) μmol/L preconception, 48.0 (9.2) μmol/L in-pregnancy, and the mean net difference 13.6 (8.2) μmol/L. Among these births, the adjusted RR of SMM or death from 23 weeks' gestation up to 42 days post-partum was 1.16 (95% confidence interval 1.14-1.30) per 1-SD (8.2 μmol/L) net blunting of glomerular hyperfiltration. LIMITATIONS: As SCr assessment is not a routine part of pregnancy care, its measurement could have been for a specific health condition thereby imparting selection bias. CONCLUSIONS: Blunted glomerular hyperfiltration in pregnancy may identify some women at higher risk of SMM. Further prospective research is needed about the implications of glomerular hyperfiltration in early pregnancy.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".