ArticleAmerican journal of translational research2021
Construction of a prediction model and a prevention control system for cesarean section rate based on the Robson classification system.
Article in American journal of translational research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo systematically explore the risk factors that influence cesarean section rate, and establish a prediction model to investigate a system effectively reducing cesarean section rates.
methodsThis retrospective study was carried out in the medical institutions in Xingtai city, where cesarean section could be conducted. The data of parturients who gave birth to children in the past five years were collected using the hospital information system. Based on the Robson's ten group classification system, parturients were grouped. The difference of cesarean section rate in each group and its main influencing factors were then analyzed. The above factors and factors such as age, education background, and knowledge on childbirth were independent variables, while cesarean section was the dependent variable. A logistic regression model was constructed to determine the correlation between relevant influencing factors and cesarean section.
resultsIn the past 5 years, cesarean section rate in Xingtai city had been maintained at a relatively high level. Cesarean section rates in the R2 and R5 groups were the highest. Parity, fetal position, number of fetuses, and gestational weeks were all factors affecting cesarean section rate (all P < 0.01). After screening the above factors using logistic regression analysis, a regression equation was established: logistic (p) = -1.061 + 1.107 * parity + 0.196 * fetal position + 2.245 * number of fetuses - 0.070 * gestational week + 0.234 * age - 0.278 * education background + 0.623 * knowledge on childbirth.
conclusionThe Robson classification system plays an important role in evaluating and supervising parturients' conditions. Based on the Robson classification system, we find that parity, fetal position, number of fetuses, and gestational weeks are the main factors influencing cesarean section rate. Using logistic regression analysis, a prediction model, with guiding significance on the control of cesarean section rate, is established.
Indexed as
Identifiers
34017494PMC8129404What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.