bluuebunny/crossref_metadata_2025_split huggingface dataset:
Correction of vaginal microbiota in patients in early and late transition to menopause
Authors: Karakhalis L. Yu. , Ponomareva Yu. S. , Ivantsiv N. S. 
Published: April 2020

Introduction: to investigate the possibility of using clindamycin and butaconazole in local therapy of moderate and severe dysbiosis in patients during the menopausal transition (early and late).Materials and methods: 107 women were examined. They were divided into two groups depending on their complains: the patients of group 1 (49.5%) had an early transition to menopause (44.7 ± 2.3 years); the patients of group 2 (50.5%) corresponded to a late transition to menopause (49.1 ± 1.5 years). The complains were evaluated. The levels of hormones (FSH, AMH, Inhibin B, estradiol) were determined. A study of the vaginal biotope was conducted by PCR ‘Femoflor 8. Statistical studies were conducted in the environment of STATISTICA 10 package (Tibco, USA). The difference in average values was considered statistically significant for p<0.05. Results: vegetative-vascular disorders were typically for patient in group 2 and detected in 74.1%. Mucosal dryness was 5.2 times more common in patients 2 group, as was dyspareunia (5.5 times more often), dysuria (14 times more often), itching in the vagina (3.5 times more often), discomfort (3.4 times often). All patient in group 2, and 51% of patient in group 1 had moderate and severe dysbiosis on the background of hypoestrogenia.Conclusion: The period of menopausal transition is characterized as a abnormalities of the vaginal biotope due to the activation of aerobes, anaerobes, and fungi of the genus Candida, and estrogen deficiency, that is more pronounced in patients with a late transition to menopause. Combined approach to the therapy these disorders allows to level out the clinical manifestation due to the consistent use of Clindacin B prolong and Ovipol Clio, but also improve the quality of life by individually selecting the duration estradiol therapy.
The influence of HRT on bone mineral dencity, condition of parodontal complex tissue, clinical implications of articulated syndrome in postmenopausal women
Authors: Shafieva I. A. , Bulgakova S. V. , Vasilkova E. V. , Shafieva A. V. 
Published: April 2020

Introduction. To evaluate the effect of hormone replacement therapy on periodontal treatment outcomes complex, bone mineral density and articular syndrome in a group of postmenopausal women.Materials and methods. We carried out an exploration of 32 postmenopausal women aged 50-59 years , with chronic periodontitis, postmenopausal osteoporosis, hand osteoarthritis. The control group (n=14) didn’t receive HRT (HRT–). The test group (n=18) –received HRT (HRT+).All subjects were examined by recording the following: plaque index (PI), sulcus bleeding index (SBI), periodontal pocket depth (PD), Dreiser`s index, VAS pain score. Measurements were recorded at the baseline, 1 month, 3 months, and 6 monthsю Results. The significant decline was revealed in all of the values (PI, SBI, PD) in HRT+ compare to HRT-. After 6, 12 months, in the periodontal area had been stabilized in the HRT+ group. 11.2% reduction was indicated in the lumbar spine and 8,15% reduction in the femoral neck within the HRTafter 1 year. The significant decrease was discovered in VAS pain score, Dreiser`s index, painful joints quantity in HRT+.Conclusions: 12-week HRT was highly effective in periodontal treatment outcomes (significant decrease in PI, SBI, PD after 3 months). BMD of the femoral neck and lumbar spine is reduced within 1 year in patients with MHTcompared with MHT +.In the group of MHT +, compared with the group of MHTand baseline data, after 3 months there was a significant improvement in the number of painful joints, the level of pain, the Dreiser index, a positive effect persists until the end of the study.
The predictors of preterm labour in patients with multiple pregnancy
Authors: Barinov S. V. , Belinina A. A. , Koliado O. V. , Molchanova I. V. , Shkret A. A. , Stepanov S. S. 
Published: April 2020

Introduction. The number of women with multiple pregnancy is increasing worldwide, especially in countries with a high level of health care, where assisted reproductive technologies are widely used. According to foreign studies, one third of twins are born as a result of Assisted Reproductive Technologies (ART), so only an increase in multiple pregnancy can be predicted in the future. The main obstetric problem with these pregnancies is the problem of carrying.Objective: To identify the predictors of preterm birth in patients with multiple pregnancy in order to improve monitoring and prophylactic measures among this cohort of women.Material and methods: A retrospective controlled observational study, including 154 patients with multiple pregnancies was carried out. Logistic analysis was used to identify the predictors of preterm labour.Results: The study showed that the predictors of preterm birth varied from one trimester to another. Based on the identified predictors, the predictive models for each trimester of pregnancy were compiled. Most of the identified predictors are related to obstetrical history. Risk groups formation, based on the identification of these predictors, is extremely important for qualified medical support. Prophylactic measures should be performed on the pre-conceptional stage. Pregnancy planning should be recommended only after treatment of chronic endometritis, followed by control of vaginal microflora and progesterone support. It is hardly possible to talk about the prevention of cervical insufficiency. However, cervical correction is an important factor for perinatal outcomes improving. The research suggests that the insertion of cervical pessary in women with multiple pregnancy and cervical insufficiency allows to prolong the gestational period for 7 weeks.Conclusion: a comprehensive approach of management of women with multiple pregnancy based on the prognostic scales of preterm labour, allows to reduce the preterm birth rate.
Efficacy of proteolytic therapy in the comprehensive management of patients with second-degree intrauterine synechia
Authors: Kravtsova E. I. , Kutsenko I. I. , Kholina L. A. , Anikina G. A. 
Published: April 2020

Introduction. The article discusses the possibilities of comprehensive management for patients with Asherman’s syndrome.Objective: to describe the experience in comprehensive management of patients with Asherman’s syndrome using an enzymatic proteolytic agent – bovgyaluronidase azoximer.Material and methods. A total of 43 patients with Asherman’s syndrome were examined. The examination methods included transvaginal ultrasound (TVUS), hysteroscopy, intrauterine dissection of synechia, endometrial histopathological examination, IL-6, IL-10, VEGF-A and TGFβ1 test in cervical mucus. The treatment included hysteroscopy, dissection of intrauterine synechia and use of Longidaza I.M. at a dose of 3000 IU once every three days for the course – 5 injections followed up by 1 supposidoitory (3000 ME) intravaginally once every two days for the course – 15 applications) on the top of already administered cyclic estrogen gestagen therapy.Results. A statistically significant reduction of anti-inflammatory index (AII) and TGFβ1/VEGF-A ratio in cervical mucus up to the test results of healthy women of reproductive age (controls) was observed in assessing changes in the local immunological status in patients with Asherman’s syndrome under pressure of azoximer bovgioluronidase therapy after 6 months of observation. AII – 0.68 (0.02) c.u. Group 1 against 0.65 (0.03) c.u. (in control), p = 0.87 and TGFβ1/VEGF-A – 1.8 (0.3) c.u. against 1.4 (0.2) c.u. accordingly, p = 0.84.Сonclusions. Analysis of clinical efficacy of azoximer bovgyaluronidase in the comprehensive management of 21 women with second-grade intrauterine synechia showed recovery of menstrual cycle in 18/21 (85.7%) patients, relief of menstrual pain in 11/21 (52.3%) patients. 6/21 (28.5%) patients with infertility and 2/21 (9.5%) patients with miscarriage obtained spontaneous pregnancy 12 months after the therapy. All pregnancies ended in term births.
Developing and Applying a Formative Evaluation Framework for Health Information Technology Implementations: Qualitative Investigation
Authors: Cresswell Kathrin , Williams Robin , Sheikh Aziz 
Published: April 2020

Background
There is currently a lack of comprehensive, intuitive, and usable formative evaluation frameworks for health information technology (HIT) implementations. We therefore sought to develop and apply such a framework. This study describes the Technology, People, Organizations, and Macroenvironmental factors (TPOM) framework we developed.

Objective
The aim was to develop and apply a formative evaluation framework for HIT implementations, highlighting interrelationships between identified dimensions and offering guidance for implementers.

Methods
We drew on an initial prototype framework developed as part of a literature review exploring factors for the effective implementation of HIT. In addition, we used qualitative data from three national formative evaluations of different HIT interventions (electronic health record, electronic prescribing, and clinical decision support functionality). The combined data set comprised 19 case studies of primarily hospital settings, and included 703 semistructured interviews, 663 hours of observations, and 864 documents gathered from a range of care settings across National Health Service (NHS) England and NHS Scotland. Data analysis took place over a period of 10 years and was guided by a framework informed by the existing evidence base.

Results
TPOM dimensions are intimately related and each include a number of subthemes that evaluators need to consider. Although technological functionalities are crucial in getting an initiative off the ground, system design needs to be cognizant of the accompanying social and organizational transformations required to ensure that technologies deliver the desired value for a variety of stakeholders. Wider structural changes, characterized by shifting policy landscapes and markets, influence technologies and the ways they are used by organizations and staff.

Conclusions
The TPOM framework supports formative evaluations of HIT implementation and digitally enabled transformation efforts. There is now a need for prospective application of the TPOM framework to determine its value.
Deep Learning Frameworks for Rapid Gram Stain Image Data Interpretation: Protocol for a Retrospective Data Analysis
Authors: Kim Hee , Ganslandt Thomas , Miethke Thomas , Neumaier Michael , Kittel Maximilian 
Published: April 2020

Background
In recent years, remarkable progress has been made in deep learning technology and successful use cases have been introduced in the medical domain. However, not many studies have considered high-performance computing to fully appreciate the capability of deep learning technology.

Objective
This paper aims to design a solution to accelerate an automated Gram stain image interpretation by means of a deep learning framework without additional hardware resources.

Methods
We will apply and evaluate 3 methodologies, namely fine-tuning, an integer arithmetic–only framework, and hyperparameter tuning.

Results
The choice of pretrained models and the ideal setting for layer tuning and hyperparameter tuning will be determined. These results will provide an empirical yet reproducible guideline for those who consider a rapid deep learning solution for Gram stain image interpretation. The results are planned to be announced in the first quarter of 2021.

Conclusions
Making a balanced decision between modeling performance and computational performance is the key for a successful deep learning solution. Otherwise, highly accurate but slow deep learning solutions can add value to routine care.

International Registered Report Identifier (IRRID)
DERR1-10.2196/16843
An Individual Cognitive Stimulation Therapy App for People With Dementia: Development and Usability Study of Thinkability
Authors: Rai Harleen Kaur , Schneider Justine , Orrell Martin 
Published: April 2020

Background
There is a lack of technological resources for the mental stimulation and communication of people with dementia, which can be helpful in improving cognition and quality of life. Paper-based individual cognitive stimulation therapy (iCST) for people with dementia has the potential to be adapted to a touchscreen format. This can improve accessibility and provide mental stimulation using interactive features. There is a need for a rigorous and systematic approach toward development, leading to improved suitability and implementation of the intervention, so that more people can benefit from its use.

Objective
This study aims to develop and investigate the usability of Thinkability, an iCST app that can be used by people with dementia and carers on touchscreen tablets.

Methods
The Medical Research Council framework for evaluating complex interventions and the Centre for eHealth Research roadmap served as frameworks for the stages of intervention and technology development. The development of the iCST app itself adopted an agile approach with elements from action research. Hence, it was developed in 3 successive sprints and was evaluated by relevant stakeholders at each sprint. Sprint 1 included 2 patient and public involvement (PPI) consultation meetings, sprint 2 included 1 PPI consultation meeting, and 4 focus groups and 10 individual interviews were organized in sprint 3. A feasibility trial is currently underway.

Results
The findings from each sprint were used to inform the development. Sprint 1 helped to identify the relevant evidence base and explored the attitudes of people with dementia and carers toward a potential iCST app. In sprint 2, an initial prototype was evaluated in a small PPI consultation meeting. In sprint 3, feedback was gathered through a qualitative study on the quality and perceived effectiveness of the iCST app. It was well received by people with dementia and carers. A need for more updated and personalized content was highlighted.

Conclusions
This study proves that an agile approach toward technology development involving all relevant stakeholders is effective in creating suitable technology. Adding to our previous knowledge of noncomputerized cognitive stimulation therapy, the release of the iCST app will make this psychosocial intervention accessible to more users worldwide.
Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development
Authors: Hou Can , Zhong Xiaorong , He Ping , Xu Bin , Diao Sha , Yi Fang , Zheng Hong , Li Jiayuan 
Published: April 2020

Background
Risk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China, but the successful implementation of such intervention requires an accurate breast cancer prediction model for Chinese women.

Objective
This study aimed to evaluate and compare the performance of four machine learning algorithms on predicting breast cancer among Chinese women using 10 breast cancer risk factors.

Methods
A dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls was used for model training and testing. We used repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance.

Results
The three novel machine-learning algorithms (XGBoost, Random Forest and Deep Neural Network) all achieved significantly higher area under the receiver operating characteristic curves (AUCs), sensitivity, and accuracy than logistic regression. Among the three novel machine learning algorithms, XGBoost (AUC 0.742) outperformed deep neural network (AUC 0.728) and random forest (AUC 0.728). Main residence, number of live births, menopause status, age, and age at first birth were considered as top-ranked variables in the three novel machine learning algorithms.

Conclusions
The novel machine learning algorithms, especially XGBoost, can be used to develop breast cancer prediction models to help identify women at high risk for breast cancer in developing countries.
An Evidence-Based, Nursing Handover Standard for a Multisite Public Hospital in Switzerland: Web-Based, Modified Delphi Study
Authors: Tacchini-Jacquier Nadine , Hertzog Hélène , Ambord Kilian , Urben Peter , Turini Pierre , Verloo Henk 
Published: April 2020

Background
Ineffective communication procedures create openings for errors when health care professionals fail to transfer complete, consistent information. Deficient or absent clinical handovers, or failures to transfer information, responsibility, and accountability, can have severe consequences for hospitalized patients. Clinical handovers are practiced every day, in many ways, in all institutional health care settings.

Objective
This study aimed to design an evidence-based, nursing handover standard for inpatients for use at shift changes or internal transfers between hospital wards.

Methods
We carried out a modified, multiround, web-based, Delphi data collection survey of an anonymized panel sample of 264 nurse experts working at a multisite public hospital in Switzerland. Each survey round was built on responses from the previous one. The surveys ended with a focus group discussion consisting of a randomly selected panel of participants to explain why items for the evidence-based clinical nursing handover standard were selected or not selected. Items had to achieve a consensus of ≥70% for selection and inclusion.

Results
The study presents the items selected by consensus for an evidence-based nursing handover standard for inpatients for use at shift changes or internal transfers. It also presents the reasons why survey items were or were not included.

Conclusions
This modified Delphi survey method enabled us to develop a consensus- and evidence-based nursing handover standard now being trialed at shift changes and the internal transfers of inpatients at our multisite public hospital in Switzerland.
Detecting Screams From Home Audio Recordings to Identify Tantrums: Exploratory Study Using Transfer Machine Learning
Authors: O'Donovan Rebecca , Sezgin Emre , Bambach Sven , Butter Eric , Lin Simon 
Published: April 2020

BackgroundQualitative self- or parent-reports used in assessing children’s behavioral disorders are often inconvenient to collect and can be misleading due to missing information, rater biases, and limited validity. A data-driven approach to quantify behavioral disorders could alleviate these concerns. This study proposes a machine learning approach to identify screams in voice recordings that avoids the need to gather large amounts of clinical data for model training.ObjectiveThe goal of this study is to evaluate if a machine learning model trained only on publicly available audio data sets could be used to detect screaming sounds in audio streams captured in an at-home setting.MethodsTwo sets of audio samples were prepared to evaluate the model: a subset of the publicly available AudioSet data set and a set of audio data extracted from the TV show Supernanny, which was chosen for its similarity to clinical data. Scream events were manually annotated for the Supernanny data, and existing annotations were refined for the AudioSet data. Audio feature extraction was performed with a convolutional neural network pretrained on AudioSet. A gradient-boosted tree model was trained and cross-validated for scream classification on the AudioSet data and then validated independently on the Supernanny audio.ResultsOn the held-out AudioSet clips, the model achieved a receiver operating characteristic (ROC)–area under the curve (AUC) of 0.86. The same model applied to three full episodes of Supernanny audio achieved an ROC-AUC of 0.95 and an average precision (positive predictive value) of 42% despite screams only making up 1.3% (n=92/7166 seconds) of the total run time.ConclusionsThese results suggest that a scream-detection model trained with publicly available data could be valuable for monitoring clinical recordings and identifying tantrums as opposed to depending on collecting costly privacy-protected clinical data for model training.
Developing a Plan for the Sustainable Implementation of an Electronic Health Intervention (Partner in Balance) to Support Caregivers of People With Dementia: Case Study
Authors: Christie Hannah Liane , Boots Lizzy Mitzy Maria , Peetoom Kirsten , Tange Huibert Johannes , Verhey Frans Rochus Josef , de Vugt Marjolein Elizabeth 
Published: April 2020

Background
Given the increasing use of digital interventions in health care, understanding how best to implement them is crucial. However, evidence on how to implement new academically developed interventions in complex health care environments is lacking. This case study offers an example of how to develop a theory-based implementation plan for Partner in Balance, an electronic health (eHealth) intervention to support the caregivers of people with dementia.

Objective
The specific objectives of this study were to (1) formulate evidence-based implementation strategies, (2) develop a sustainable business model, and (3) integrate these elements into an implementation plan.

Methods
This case study concerns Partner in Balance, a blended care intervention to support the caregivers of people with dementia, which is effective in improving caregiver self-efficacy, quality of life, and experienced control. The large-scale implementation of Partner in Balance took place in local dementia case-management services, local care homes, dementia support groups, and municipalities. Experiences from real-life pilots (n=22) and qualitative interviews with national stakeholders (n=14) were used to establish an implementation plan consisting of implementation strategies and a business model.

Results
The main finding was the need for a business model to facilitate decision-making from potential client organizations, who need reliable pricing information before they can commit to training coaches and implementing the intervention. Additionally, knowledge of the organizational context and a wider health care system are essential to ensure that the intervention meets the needs of its target users. Based on these findings, the research team formulated implementation strategies targeted at the engagement of organizations and staff, dissemination of the intervention, and facilitation of long-term project management in the future.

Conclusions
This study offers a theory-based example of implementing an evidence-based eHealth intervention in dementia health care. The findings help fill the knowledge gap on the eHealth implementation context for evidence-based eHealth interventions after the trial phase, and they can be used to inform individuals working to develop and sustainably implement eHealth.