Monitoring Renal Function in Hypertensive Pregnancy
Bibliographic record
Abstract
OBJECTIVE: In hypertensive pregnancy, to compare 24hr creatinine clearance (CrCl) with formulae-derived renal function (Cockcroft-Gault (CG) or Modified Diet in Renal Disease (MDRD)). STUDY DESIGN: Retrospective review (198 women, 63% preeclampsia) using paired t-test (significant p < 0.008) and intraclass correlation coefficients (acceptable >0.70) to compare 24hr and CG CrCl. The 24hr CrCl was compared with each of the CG and MDRD formulae by Bland-Altman plots. RESULTS: For 24hr CrCl, uncorrected values were similar to corrected using pre-pregnancy weight (p = 0.04); other weights gave consistently different CrCl (p < 0.0001). Limits of agreement were wide when CG and MDRD formulae were compared with 24hr CrCl. Compared with 24hr CrCl, MDRD estimates were consistently lower, and CG CrCl higher (current weight) or lower (pre-pregnancy or lean weight). CONCLUSION: MDRD and CG formulae should not be used in hypertensive pregnancy. Use of serum creatinine is advocated. If 24hr CrCl is performed, any correction should utilize pre-pregnancy weight. Neither the Cockcroft-Gault nor Modified Diet in Renal Disease formulae for glomerular filtration rate estimation are alternatives to 24hr creatinine clearance in hypertensive pregnancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".