Academic literature on the topic 'Singleton Training Area'
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Journal articles on the topic "Singleton Training Area"
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Fu, Wei, Shuang Yu, and Xin Wang. "A Novel Method to Determine Basic Probability Assignment Based on Adaboost and Its Application in Classification." Entropy 23, no. 7 (June 25, 2021): 812. http://dx.doi.org/10.3390/e23070812.
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In the framework of evidence theory, one of the open and crucial issues is how to determine the basic probability assignment (BPA), which is directly related to whether the decision result is correct. This paper proposes a novel method for obtaining BPA based on Adaboost. The method uses training data to generate multiple strong classifiers for each attribute model, which is used to determine the BPA of the singleton proposition since the weights of classification provide necessary information for fundamental hypotheses. The BPA of the composite proposition is quantified by calculating the area ratio of the singleton proposition’s intersection region. The recursive formula of the area ratio of the intersection region is proposed, which is very useful for computer calculation. Finally, BPAs are combined by Dempster’s rule of combination. Using the proposed method to classify the Iris dataset, the experiment concludes that the total recognition rate is 96.53% and the classification accuracy is 90% when the training percentage is 10%. For the other datasets, the experiment results also show that the proposed method is reasonable and effective, and the proposed method performs well in the case of insufficient samples.
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Li, Yun-Xiu, Zhi Bai, Da-Jian Long, Hai-Bo Wang, Yang-Feng Wu, Kathleen H. Reilly, Su-Ran Huang, and Yan-Jie Ji. "Predicting the success of vaginal birth after caesarean delivery: a retrospective cohort study in China." BMJ Open 9, no. 5 (May 2019): e027807. http://dx.doi.org/10.1136/bmjopen-2018-027807.
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ObjectivesTo develop a nomogram to predict the likelihood of vaginal birth after caesarean section (VBAC) among women after a previous caesarean section (CS).DesignA retrospective cohort study.SettingTwo secondary hospitals in Guangdong Province, China.ParticipantsInclusion criteria were as follows: pregnant women with singleton fetus, age ≥18 years, had a history of previous CS and scheduled for trial of labour after caesarean delivery (TOLAC). Patients with any of the following were excluded from the study: preterm labour (gestational age <37 weeks), two or more CSs, contradictions for vaginal birth, history of other uterine incision such as myomectomy, and incomplete medical records.Primary outcome measureThe primary outcome was VBAC, which was retrospectively abstracted from computerised medical records by clinical staff.ResultsOf the women who planned for TOLAC, 84.0% (1686/2006) had VBAC. Gestational age, history of vaginal delivery, estimated birth weight, body mass index, spontaneous onset of labour, cervix Bishop score and rupture of membranes were independently associated with VBAC. An area under the receiver operating characteristic curve (AUC) in the prediction model was 0.77 (95% CI 0.73 to 0.81) in the training cohort. The validation set showed good discrimination with an AUC of 0.70 (95% CI 0.60 to 0.79).ConclusionsTOLAC may be a potential strategy for decreasing the CS rate in China. The validated nomogram to predict success of VBAC could be a potential tool for VBAC counselling.
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Stroeder, Russalina, Julia Radosa, Lea Clemens, Christoph Gerlinger, Gilda Schmidt, Panagiotis Sklavounos, Zoltan Takacs, Gabriele Meyberg-Solomayer, Erich-Franz Solomayer, and Amr Hamza. "Urogynecology in obstetrics: impact of pregnancy and delivery on pelvic floor disorders, a prospective longitudinal observational pilot study." Archives of Gynecology and Obstetrics 304, no. 2 (March 22, 2021): 401–8. http://dx.doi.org/10.1007/s00404-021-06022-w.
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Abstract Purpose To assess changes in the pelvic floor anatomy that cause pelvic floor disorders (PFDs) in primigravidae during and after pregnancy and to evaluate their impact on women’s quality of life (QoL). Methods POP-Q and translabial ultrasound examination was performed in the third trimester and 3 months after delivery in a cohort of primigravidae with singleton pregnancy delivering in a tertiary center. Results were analyzed regarding mode of delivery and other pre- and peripartal factors. Two individualized detailed questionnaires were distributed at 3 months and at 12 months after childbirth to determinate QoL. Results We recruited 45 women, of whom 17 delivered vaginally (VD), 11 received a vacuum extraction delivery (VE) and 17 a Cesarean section in labor (CS). When comparing third-trimester sonography to 3 months after delivery, bladder neck mobility increased significantly in each delivery group and hiatal area increased significantly in the VD group. A LAM avulsion was found in two women after VE. Connective tissue weakness (p = 0.0483) and fetal weight at birth (p = 0.0384) were identified as significant risk factors for the occurrence of PFDs in a multivariant regression analysis. Urinary incontinence was most common with 15% and 11% of cases at 3, respectively, 12 months after delivery. 42% of women reported discomfort during sexual intercourse, 3 months after delivery and 24% 12 months postpartum. Although 93% of women engage a midwife after delivery, only 56% participated in pelvic floor muscle training. Conclusion Connective tissue weakness and high fetal weight at birth are important risk factors for the occurrence of PFDs. Nevertheless, more parturients should participate in postpartal care services to prevent future PFDs.
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Arabi Belaghi, Reza, Joseph Beyene, and Sarah D. McDonald. "Prediction of preterm birth in nulliparous women using logistic regression and machine learning." PLOS ONE 16, no. 6 (June 30, 2021): e0252025. http://dx.doi.org/10.1371/journal.pone.0252025.
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Objective To predict preterm birth in nulliparous women using logistic regression and machine learning. Design Population-based retrospective cohort. Participants Nulliparous women (N = 112,963) with a singleton gestation who gave birth between 20–42 weeks gestation in Ontario hospitals from April 1, 2012 to March 31, 2014. Methods We used data during the first and second trimesters to build logistic regression and machine learning models in a “training” sample to predict overall and spontaneous preterm birth. We assessed model performance using various measures of accuracy including sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) in an independent “validation” sample. Results During the first trimester, logistic regression identified 13 variables associated with preterm birth, of which the strongest predictors were diabetes (Type I: adjusted odds ratio (AOR): 4.21; 95% confidence interval (CI): 3.23–5.42; Type II: AOR: 2.68; 95% CI: 2.05–3.46) and abnormal pregnancy-associated plasma protein A concentration (AOR: 2.04; 95% CI: 1.80–2.30). During the first trimester, the maximum AUC was 60% (95% CI: 58–62%) with artificial neural networks in the validation sample. During the second trimester, 17 variables were significantly associated with preterm birth, among which complications during pregnancy had the highest AOR (13.03; 95% CI: 12.21–13.90). During the second trimester, the AUC increased to 65% (95% CI: 63–66%) with artificial neural networks in the validation sample. Including complications during the pregnancy yielded an AUC of 80% (95% CI: 79–81%) with artificial neural networks. All models yielded 94–97% negative predictive values for spontaneous PTB during the first and second trimesters. Conclusion Although artificial neural networks provided slightly higher AUC than logistic regression, prediction of preterm birth in the first trimester remained elusive. However, including data from the second trimester improved prediction to a moderate level by both logistic regression and machine learning approaches.
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Bell, Ruth, Svetlana V. Glinianaia, Zelda van der Waal, Andrew Close, Eoin Moloney, Susan Jones, Vera Araújo-Soares, et al. "Evaluation of a complex healthcare intervention to increase smoking cessation in pregnant women: interrupted time series analysis with economic evaluation." Tobacco Control 27, no. 1 (February 15, 2017): 90–98. http://dx.doi.org/10.1136/tobaccocontrol-2016-053476.
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ObjectivesTo evaluate the effectiveness of a complex intervention to improve referral and treatment of pregnant smokers in routine practice, and to assess the incremental costs to the National Health Service (NHS) per additional woman quitting smoking.DesignInterrupted time series analysis of routine data before and after introducing the intervention, within-study economic evaluation.SettingEight acute NHS hospital trusts and 12 local authority areas in North East England.Participants37 726 records of singleton delivery including 10 594 to mothers classified as smoking during pregnancy.InterventionsA package of measures implemented in trusts and smoking cessation services, aimed at increasing the proportion of pregnant smokers quitting during pregnancy, comprising skills training for healthcare and smoking cessation staff; universal carbon monoxide monitoring with routine opt-out referral for smoking cessation support; provision of carbon monoxide monitors and supporting materials; and an explicit referral pathway and follow-up protocol.Main outcome measuresReferrals to smoking cessation services; probability of quitting smoking during pregnancy; additional costs to health services; incremental cost per additional woman quitting.ResultsAfter introduction of the intervention, the referral rate increased more than twofold (incidence rate ratio=2.47, 95% CI 2.16 to 2.81) and the probability of quitting by delivery increased (adjusted OR=1.81, 95% CI 1.54 to 2.12). The additional cost per delivery was £31 and the incremental cost per additional quit was £952; 31 pregnant women needed to be treated for each additional quitter.ConclusionsThe implementation of a system-wide complex healthcare intervention was associated with significant increase in rates of quitting by delivery.
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Costantine, Maged M., Grecio Sandoval, William A. Grobman, Jennifer L. Bailit, Uma M. Reddy, Ronald J. Wapner, Michael W. Varner, et al. "A Model to Predict Vaginal Delivery and Maternal and Neonatal Morbidity in Low-Risk Nulliparous Patients at Term." American Journal of Perinatology, October 19, 2020. http://dx.doi.org/10.1055/s-0040-1718704.
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Objective This study aimed to develop and validate a model to predict the probability of vaginal delivery (VD) in low-risk term nulliparous patients, and to determine whether it can predict the risk of severe maternal and neonatal morbidity. Methods Secondary analysis of an obstetric cohort of patients and their neonates born in 25 hospitals across the United States (n = 115,502). Trained and certified research personnel abstracted the maternal and neonatal records. Nulliparous patients with singleton, nonanomalous vertex fetuses, admitted with an intent for VD ≥ 37 weeks were included in this analysis. Patients in active labor (cervical exam > 5 cm), those with prior cesarean and other comorbidities were excluded. Eligible patients were randomly divided into a training and test sets. Based on the training set, and using factors available at the time of admission for delivery, we developed and validated a logistic regression model to predict the probability of VD, and then estimated the prevalences of severe morbidity according to the predicted probability of VD. Results A total of 19,611 patients were included. Based on the training set (n = 9,739), a logistic regression model was developed that included maternal age, body mass index (BMI), cervical dilatation, and gestational age on admission. The model was internally validated on the test set (n = 9,872 patients) and yielded a receiver operating characteristic-area under the curve (ROC-AUC) of 0.71 (95% confidence interval [CI]: 0.70–0.72). Based on a subset of 18,803 patients with calculated predicted probabilities, we demonstrated that the prevalences of severe morbidity decreased as the predicted probability of VD increased (p < 0.01). Conclusion In a large cohort of low-risk nulliparous patients in early labor or undergoing induction of labor, at term with singleton gestations, we developed and validated a model to calculate the probability of VD, and maternal and neonatal morbidity. If externally validated, this calculator may be clinically useful in helping to direct level of care, staffing, and adjustment for case-mix among various systems. Key Points
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Roberts, Mark. "The Institute of Archaeology Field Course 2014: The Search for the Lost Hunting Lodge of the Earls of Arundel at Downley, Singleton, West Sussex, UK." Archaeology International, October 23, 2014. http://dx.doi.org/10.5334/ai.1722.
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This paper discusses research into the area of the medieval and post-medieval deer park, at Downley in West Sussex, which is the focus of the Institute’s current training excavations. The excavations constitute part of a wider project attempting to learn more about the development and function of the park through time. A range of information, which indicated the presence of a substantial lodge building within the park was considered and the 2014 excavations have revealed evidence almost certainly corroborating its presence. The project will continue both on and off site in 2015.
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Howbert, James Jeffry, Ellen Kauffman, Kristin Sitcov, and Vivienne Souter. "A Simple Approach to Adjust for Case-Mix when Comparing Institutional Cesarean Birth Rates." American Journal of Perinatology, November 4, 2019. http://dx.doi.org/10.1055/s-0039-1697590.
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Abstract Objective To develop a validated model to predict intrapartum cesarean in nulliparous women and to use it to adjust for case-mix when comparing institutional laboring cesarean birth (CB) rates. Study Design This multicenter retrospective study used chart-abstracted data on nulliparous, singleton, term births over a 7-year period. Prelabor cesareans were excluded. Logistic regression was used to predict the probability of CB for individual pregnancies. Thirty-five potential predictive variables were evaluated including maternal demographics, prepregnancy health, pregnancy characteristics, and newborn weight and gender. Models were trained on 21,017 births during 2011 to 2015 (training cohort), and accuracy assessed by prediction on 15,045 births during 2016 to 2017 (test cohort). Results Six variables delivered predictive success equivalent to the full set of 35 variables: maternal weight, height, and age, gestation at birth, medically-indicated induction, and birth weight. Internal validation within the training cohort gave a receiver operator curve with area under the curve (ROC-AUC) of 0.722. External validation using the test cohort gave ROC-AUC of 0.722 (0.713–0.731 confidence interval). When comparing observed and predicted CB rates at 16 institutions in the test cohort, five had significantly lower than predicted rates and three had significantly higher than predicted rates. Conclusion Six routine clinical variables used to adjust for case-mix can identify outliers when comparing institutional CB rates.
Dissertations / Theses on the topic "Singleton Training Area"
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Gourlay, Robert C., and n/a. "Environmental assessment for land use management : the development and application of environmental assessment methods and techniques at the Singleton Training Area (STA)- Army." University of Canberra. Resource, Environmental & Heritage Sciences, 1997. http://erl.canberra.edu.au./public/adt-AUC20061113.153454.
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Methods and techniques for environmental impact assessment (EIA) and development of land use
management options are described. The methods and techniques have specific application in Defence
estate management, and general application in other areas of land use assessment and management.
The EIA methodology includes techniques for land cover and soil classification, land capability and
suitability assessment. The biophysical classifications and assessment techniques are based on the
application of various para and non- parametric approaches. The study area for the application of the
EIA methods and techniques was the Singleton Training Area (STA) in the Hunter Valley of central
New South Wales.
Defence estates are required to provide a wide range of terrain and other environmental conditions to
support the development of combat related tactics. The maintenance of these areas for sustainable use
is fundamental in achieving both military and land use management objectives. The EIA of the STA
provided a means of testing the efficiency of the methods and techniques developed in this thesis.
The baseline resource inventory data used in the EIA includes land cover and vegetation maps derived
from satellite digital data and soils maps derived from both conventional methods and airborne gammaradiation
data. This information, together with the military land use requirements provided the basis
for land capability and suitability assessment, and development of land use management options.
Book chapters on the topic "Singleton Training Area"
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Outlaw, Veronica, Margaret L. Rice, and Vivian H. Wright. "Building Quality Online Courses." In Handbook of Research on Building, Growing, and Sustaining Quality E-Learning Programs, 301–23. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0877-9.ch015.
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The authors have personally experienced the burdens of creating online courses without the assistance of trained instructional designers. Many faculty are employed in conditions where they develop online courses without the proper knowledge and training of many essential aspects that should accompany the development of an online course (e.g., design, development, learning styles, copyright, design principles, accessibility, incorporating technology, and technical skills) (Singleton et al., 2013; Speck, 2000; Wickersham et al., 2007). The authors propose and describe the use of a detailed online course development and delivery model (Outlaw & Rice, 2015) that was created to provide distance education units with a systematic approach to course development. The proposed model nourishes the partnerships between faculty and designers to improve the quality of online courses, while providing the support faculty need to successfully create online courses. The authors discuss a model that provides consistency in efforts to create efficient workflows to develop online courses.