The Results of a Prospective Cohort Study of the Effectiveness of the Algorithm for Monitoring Pregnancies in Patients from the Group of High Perinatal Risk to Reduce Perinatal Losses and Improve Neonatal Outcome
Bibliographic record
Abstract
Prenatal prognosis is an important part of obstetric care, which aims to reduce fetal and neonatal losses. A differentiated approach to the management of different risk groups allows you to optimize existing approaches. The objective: сomparison of pregnancy results in the high perinatal risk group using the proposed monitoring algorithms and the traditional method of management in a prospective cohort study. Materials and methods. The prospective cohort study was conducted from 2016 to 2018 on the basis of the medical center LLC «Uniclinica», Medical Genetics Center «Genome», Clinic of Reproductive Genetics «Victoria», Kyiv City Maternity Hospital №2. 580 women were included in the final analysis. Exclusion criteria were: low risk (0–2) according to the adapted antenatal risk scale (Alberta perinatal health program), multiple pregnancy, critical malformations and chromosomal abnormalities of the fetus, lack of complete information about the outcome of pregnancy, lack of results of all intermediate clinical and laboratory surveys. Results. The introduction of a comprehensive differentiated approach has improved the diagnosis of late forms of growth retardation (OR 4,14 [1.42–12.09]; p=0,009), reduced the frequency of urgent cesarean sections (OR 1,61 [1,03–2,49]; p=0,046) and reduced perinatal mortality [1,09–21,3]; р=0,041) due to reduction of antenatal losses (OR 2,2 [1,06–4,378]; р=0,045). There was a significant increase in the frequency of planned cesarean sections (p<0,0001, without affecting the total number of operative deliveries) and statistically insignificant, but tendentiously clear shifts to the increase in the frequency of preterm birth between 34–37 weeks of pregnancy and intensive care unit. The latter observation can be explained by better diagnosis of threatening fetal conditions and an increase in the frequency of active obstetric tactics, which in turn affects the number of premature infants, the involvement of the neonatal service, and thus the intensification of the load on intensive. Conclusions. Adequate enhanced monitoring should combine ultrasound, cardiotocography, actography and laboratory techniques, each of which will have a clearly defined purpose in a combined approach to fetal assessment.
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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 | 0.007 | 0.013 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".