Predictors of Obstetrical Complications: A Prospective Cohort Study
Bibliographic record
Abstract
Background: The objective was to identify predictors of obstetrical complications. Methods: A prospective cohort study, a state-run obstetrical hospital, Omsk, Russia. Out of a total 2,497 pregnant women with a gestation age of less than 12 weeks, 2,356 women with singlet pregnancies were selected. Data at gestation outcome were available for 2,177 women. Medical and social factors were evaluated and blood coagulation screening was performed in women in the first trimester. Ordered logistic regression was used to assess risks of obstetrical complications. The following obstetrical complications were evaluated after 28 weeks of gestation: preeclampsia, abruptio placentae, preterm labor, intrauterine growth restrictio n a nd intrauterine hypoxia. Results: A prognostic model comprised of 21 predictors (P < 0.001) with strong association (0.82) between the studied parameters and the obstetrical complications was obtained. Probability of obstetrical complications (91.1%) was associated with the following parameters in women: age, education, parity, medical history, heredity (family history ) and blood coagulation profile. Increased fibrinogen concentrations and reduced thrombin clotting time in the first trimester w ere associated with severe obstetrical complications late in pregnancy. Conclusions: Women with late obstetrical complications were found to have increased blood viscosity and an increase in coagulation potential early in pregnancy. The prognostic model obtained may have great significance and requires further validation. J Clin Gynecol Obstet. 2014;3(1):14-21 doi: http://dx.doi.org/10.14740/jcgo202w
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".