Analysis of risk factors of preeclampsia with serious complications
Bibliographic record
Abstract
Objective To explore the risk factors of preeclampsia with serious complications. Methods Clinical data of 805 preeclampsia patients and the perinatal infants were retrospective analyzed. Three hundred and twenty-seven cases with serious complications were in the research group, 478 cases without serious complications were in the control group. Results (1) The incidence of preeclampsia patients with serious complications was 40.6%(327/805 cases). The serious complications of 327 cases were including fetal death (138 cases, 42.2%), HELLP syndrome(71 cases, 21.7%), placental abruption(65 cases, 19.9%), cardiac failure(39 cases, 11.9%), postpartum hemorrhage(39 cases, 11.9%), pneumonedema(36 cases, 11.0%) and so on. (2) The clinical manifestation of the two groups, including onset gestational age, systolic pressure, diastolic pressure, creatinine, alanine aminotransferase, albumin and expecting treatment time had statistical significance, P<0.05. (3) In multivariate logistic regression analysis, the risk factors of preeclampsia serious complications were the gestational age of early onset (OR=0.783, 95%CI: 0.745-0.823), high serum creatinine (OR=1.005, 95%CI: 1.001-1.008), hypoproteinemia (OR=0.961, 95%CI: 0.929-0.994). Conclusion The risk factors of preeclampsia with serious complications were the gestational age of early onse, kidney function damage and hypoproteinemia. Key words: Pre-eclampsia; Pregnancy complications; Risk factors
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".