Comparison of Kidney Function between Gestational Hypertension and Preeclampsia
Bibliographic record
Abstract
P regnancy-induced hypertension (PIH), which ischaracterized by hypertension and proteinuria, is a multifactor disorder and one of the main causes of perinatal and maternal morbidity and mortality [1].PIH complicates 3.0-4.6% of Japanese pregnancies [2].Gestational hypertension (GH) is thought to be different from preeclampsia (PE) in many countries, including the United States and Canada, according to The American Congress of Obstetricians and Gynecologists (http://www.acog.org/Resources-And-Publications/Task-Force-and-Work-Group-Reports/ Hypertensionin-Pregnancy; accessed June 1, 2016) and the Journal of Obstetrics and Gynecology Canada (http://sogc.org/wp-content/uploads/2013/01/ui206CPG0803hypertensioncorrection.pdf;accessed June 1, 2016).However, in Japan GH and PE are usually treated as the same disease (i.e., PIH).GH/PE is classified as a sub-classification of PIH, but GH and PE are treated basically as PIH with superimposed preeclampsia, eclampsia, and management.Methods and guidance specific to PE or GH are not used in Japan.We conducted the present study to determine whether there are any differences in perinatal outcomes, fetal growth, and maternal kidney function between pregnancies with PE and those with GH in order to investigate the
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".