Authors' Reply: Renal Function and Adverse Maternal and Fetal Outcomes: New Evidence
Bibliographic record
Abstract
We thank you for giving us the opportunity to respond to the Letter to the Editor regarding our recent publication on maternal and fetal outcomes in CKD pregnancies in Ontario.1 The authors, correctly, point out that we only studied the effect of CKD on maternal and fetal pregnancy outcomes but not the effect of pregnancy on CKD progression.2 As practicing obstetric-focused nephrologists, we have a deep appreciation for the role that pregnancy may play in CKD progression and that the current published literature does not adequately quantify this risk. Members of our group (L.B., A.G., and M.H.) have already begun to analyze CKD progression outcomes using updated data from this cohort, so stay tuned. Regarding the letter writers' second concern, we agree that knowing more about GFR changes in pregnancy will enhance our understanding of the interplay between kidney function, placental development, and maternal/fetal outcomes. Because we used real-world data for this study, and serial assessments of kidney function are not part of the standard of care in pregnancy in Canada (or elsewhere in the world), we do not have this information available to analyze. One of our authors (J.T.) is currently studying longitudinal changes in renal filtration markers across pregnancy in two large pregnancy cohorts as part of a National Institutes of Health–funded study on kidney disease in pregnancy. Thank you for reading our paper and taking the time to draw attention to the need for ongoing studies to improve maternal fetal outcomes in our patients with CKD.
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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 | 0.009 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.027 | 0.035 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".