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Effects of ovarian cyst types on ovarian reserve after three-dimensional laparoscopic cystectomy
No full textObjective: This study aims to compare the effects of three-dimensional (3D) laparoscopic ovarian cystectomy on ovarian reserve according to different types of ovarian cysts. Materials and Methods: Participants who underwent surgical treatment for ovarian cysts between 2018 and 2020 were included in this study. Antim & uuml;llerian hormone (AMH) and follicle-stimulating hormone (FSH) levels were measured before surgery and six months postoperatively. All procedures were performed under general anesthesia using 3D laparoscopy. Participants were classified into three groups based on histopathological findings: group 1, endometriomas; group 2, mature cystic teratomas (dermoid cysts); and group 3, serous or mucinous cystadenomas. Results: A total of 51 women were included in the study. No significant differences were observed between the groups in terms of perioperative variables such as operation time, intraoperative blood loss, postoperative hemoglobin decrease, and maximum cyst diameter. There were also no significant differences among the groups in preoperative AMH (p=0.97) and FSH (p=0.22) levels. Postoperative AMH levels were significantly lower than preoperative values in both the endometrioma group (p<0.001) and the dermoid cyst group (p=0.004). The reduction in AMH levels was more pronounced in the endometrioma group compared to the other groups. Postoperative FSH levels tended to increase in all groups compared to preoperative levels; however, this increase was not statistically significant (p=0.092). Conclusion: 3D laparoscopic cystectomy for the removal of endometriomas and dermoid cysts significantly reduces ovarian reserve. In contrast, laparoscopic cystectomy for serous or mucinous cysts appears to have no significant impact on ovarian reserve.Ege University Scientific Research Projects [17-TIP-056]Financial Disclosure: This work was supported by the Ege University Scientific Research Projects (grant number: 17-TIP-056)
Childhood Epilepsies with Occipital Discharges: Evaluation of 84 Patients
No full textAim: Childhood epilepsy with occipital discharges encompasses various subtypes, including childhood occipital visual epilepsy (COVE), self-limited epilepsy with autonomic seizures (SeLEAS), photosensitive occipital lobe epilepsy, symptomatic epilepsy, and unclassified cases. The primary aim of this study was to analyze the clinical characteristics of pediatric epilepsy patients with occipital discharges based on their etiological classification and to compare any differences between these subgroups. Additionally, this study sought to identify prognostic factors by comparing patients who achieved remission within 36 months (Group 1) with those who did not respond within the same period (Group 2). Materials and Methods: This study included 84 children diagnosed with occipital discharge-related epilepsy. A comprehensive review of their medical records was conducted, assessing their demographic data, ictal symptoms, neurological examination findings, electroencephalography and magnetic resonance imaging results, family history, febrile seizures, and treatment responses. Results: Of the total cohort, 32% (n=27) were classified as Group 1, while 68% (n=57) were in Group 2. Structural brain abnormalities were significantly more prevalent in Group 2. The age at diagnosis was significantly younger in Group 2 compared to Group 1 (p=0.003), and the rate of intellectual disability was higher in Group 2 (p=0.05). The presence of systemic diseases and the use of multiple anti-epileptic drugs were significantly more frequent in Group 2 (p=0.021, p=0.018). The duration of epilepsy follow-up was notably longer in Group 2 (p<0.001). COVE and SeLEAS were more commonly found in the early remission group (p=0.012, p=0.034), while no cases of symptomatic occipital epilepsy achieved remission within the first 36 months (p=0.001). Conclusion: The majority of children with occipital epilepsy did not achieve remission within 36 months. Younger age at onset and the presence of intellectual disability were associated with longer periods of non-remission. COVE and SeLEAS were more likely to achieve early remission, whereas symptomatic occipital epilepsies showed no remission within the first 36 months. These findings underline the importance of early diagnosis and highlight the potential impact of structural brain abnormalities and cognitive impairments on the prognosis of childhood occipital epilepsy. © 2025 Elsevier B.V., All rights reserved
Intensive care unit machine learning approach delirium risk assessments
No full textDeliryum, ani başlangıçlı bilişsel bozukluklar, dikkat ve farkındalık değişiklikleri ile karakterize, yoğun bakım ünitelerinde (YBÜ) morbidite ve mortaliteyi artıran önemli bir sendromdur. Bu çalışma, YBÜ hastalarında makine öğrenimi (ML) yaklaşımıyla deliryum riskinin belirlenmesini amaçlamıştır. Tanımlayıcı ve kesitsel tipte yürütülen çalışmaya, bir devlet hastanesinin 2. ve 3. basamak YBÜ'nde yatan 150 hasta dâhil edilmiştir. Araştırmanın verileri; Hasta Tanıtıcı Bilgi Formu, Yoğun Bakım Ünitesinde Konfüzyon Değerlendirme Ölçeği (CAM-ICU), Richmond Sedasyon Ajitasyon Skalası (RASS), Görsel Analog Skala (VAS), Davranışsal Ağrı Skalası (DAS), Wong-Baker Ağrı Skalası, Akut Fizyoloji ve Kronik Sağlık Değerlendirmesi (APACHE II), Ardışık Organ Yetmezliği Değerlendirmesi (SOFA) ve Hemşirelik Deliryum Tarama Skalası (Nu-DESC) kullanılarak toplanmıştır. Deliryum gelişimini tahmin etmek amacıyla Logistik Regresyon, Random Forest ve XGBoost denetimli makine öğrenmesi modelleri eğitilmiş ve karşılaştırılmıştır. Katılımcıların sosyodemografik ve klinik özelliklerine göre deliryum gelişmesinde yatış tanısı (p=0.008), mekanik ventiltasyon kullanımı (p=0.006) ve uyku süresi (p=0.000) açısından istatistiksel olarak anlamlı fark saptanmıştır. Diğer sosyodemografik ve klinik değişkenler arasında anlamlı farklılık yoktur (p>0.05). Makine öğrenimi modellerinin performans metriklerine göre Lojistik Regresyon ve Random Forest modelleri %93.3, XGBoost modeli ise %90 doğrulukla deliryum gelişimini tahmin etmede yüksek doğruluk sergilemiştir. Tüm modellerin ROC-AUC skorlarının 0.97'nin üzerinde olduğu tespit edilmiştir. Modellerin değişken önem sıralaması analizlerine göre özellikle Nu-DESC ve RASS skorları, deliryum gelişimi tahmininde diğer değişkenlere göre en yüksek öneme sahip değişkenler olarak öne çıkmıştır. Modellerin olasılık tahminleri, modellerin deliryum gelişme riskine göre hasta grubunu "düşük risk" ve "yüksek risk" olarak net şekilde ayrıştırabildiğini göstermiştir. Çalışma sonucunda, yoğun bakım hastalarında deliryum gelişimini öngörmek amacıyla geliştirilen makine öğrenimi modelleri (Lojistik Regresyon, Random Forest ve XGBoost), elde edilen veri setinde oldukça yüksek başarı sergilemiştir. Modellerin deliryum riskinin belirlenmesinde yüksek doğruluğu ve güçlü ayrıştırıcı yeteneği, olasılık skorlarının güvenle kullanılabileceğini göstermektedir. Deliryum yönetiminde kanıta dayalı hemşirelik uygulamalarına yapay zekâ araçlarının entegre edilmesi, yatak başı karar alma süreçlerini kolaylaştırarak YBÜ'nde bireyselleştirilmiş deliryum riskinin belirlenmesini sağlayabilir. Makine öğrenimi yaklaşımının hemşirelik iş yükünü azaltacağı ve maliyet etkin bir bakım sağlayacağı öngörülmektedir. Gelecekte yapılacak araştırmalar, tahmin skoruna dayalı müdahalelerin mortalite, ventilatör gün sayısı ve yoğun bakım kalış süresi üzerindeki etkilerini ölçmeye odaklanabilir. Ayrıca derin sedasyondaki hastaların modele dâhil edilmesi yönünde protokoller geliştirilmesi önerilmektedir.Delirium is a significant syndrome characterised by sudden onset cognitive impairments, changes in attention and awareness, which increases morbidity and mortality in intensive care units (ICUs). This study aimed to determine the risk of delirium in ICU patients using a machine learning (ML) approach. This descriptive, cross-sectional study included 150 patients hospitalized in a tertiary ICU of a public hospital. Data were collected using the Patient Identification Form, Confusion Assessment Scale in the Intensive Care Unit (CAM-ICU), Richmond Sedation Agitation Scale (RASS), Visual Analog Scale (VAS), Behavioral Pain Scale (DAS), Wong-Baker Pain Scale, Acute Physiology and Chronic Health Evaluation (APACHE II), Sequential Organ Failure Assessment (SOFA), and Nursing Delirium Screening Scale (Nu-DESC). Logistic regression, random forest, and XGBoost supervised machine learning models were trained and compared to predict the development of delirium. According to the sociodemographic and clinical characteristics of the participants, a statistically significant difference was found in terms of hospitalization diagnosis (p=0.008), use of mechanical ventilation (p=0.006), and sleep duration (p=0.000) in the development of delirium. There were no significant differences between other sociodemographic and clinical variables (p>0.05). According to the performance metrics of the machine learning models, the Logistic Regression and Random Forest models demonstrated high accuracy in predicting the development of delirium with 93.3% accuracy, while the XGBoost model showed 90% accuracy. The ROC-AUC scores of all models were found to be above 0.97. According to the analysis of the variable importance rankings of the models, Nu-DESC and RASS scores, in particular, stood out as the variables with the highest importance compared to other variables in predicting the development of delirium. The probability estimates of the models showed that the models could clearly distinguish the patient groups as "low risk" and "high risk" based on the risk of developing delirium. As a result of the study, machine learning models' high accuracy and strong discriminatory ability in determining delirium risk demonstrate that probability scores can be used with confidence. The integration of artificial intelligence tools into evidence-based nursing practices in the management of delirium can facilitate bedside decision-making processes, thereby enabling the determination of individualised delirium risk in the ICU. It is anticipated that the machine learning approach will reduce the workload of nurses and provide cost-effective care. Future research could measure the effects of predictive score-based interventions on mortality, ventilator days, and length of ICU stay. Furthermore, protocols for including deeply sedated patients in the model are recommended
Patients' perspectives on artifical intelligence and robot nurses
No full textBu çalışma, İzmir Bakırçay Üniversitesi Çiğli Eğitim ve Araştırma Hastanesi dahili servislerinde yatmakta olan hastaların yapay zeka ve robotik hemşirelik uygulamalarına yönelik tutumlarını ele almak amacıyla gerçekleştirilen tanımlayıcı-kesitsel bir araştırmadır. Çalışma, Aralık 2023- Aralık 2024 tarihleri arasında dahili servislerde yatan toplamda 226 hasta ile gerçekleştirilmiştir. Veri toplama formunda demografik özelliklerini belirlemek amacıyla 4 sorulu "Tanıtıcı Bilgi Formu", yapay zeka ve robotik hemşirelik uygulamalarına yönelik tutumlarını ele almak için 35 sorulu 'Robot Hemşirelere İlişkin Sorular'' yer almıştır. Araştırmanın verilerini değerlendirmede IBM SPSS Statistic 25.0 Programı kullanılmıştır. Verileri değerlendirilirken tanımlayıcı istatistiksel metotları (sayı, yüzde, min-maks değerleri, ortalama ve standart sapma, ki-kare) kullanılmıştır. Cinsiyete göre farklılık gösterip göstermediği Ki-kare testi ile analiz edilmiştir. Gruplar arası farklılığın nereden kaynaklandığının tespit edilmesi amacıyla Fisher exact testi kullanılmıştır. p<0,05 değeri anlamlı olarak kabul edilmiştir. Araştırmanın sonucunda; katılımcıların eğitim sonrası cinsiyete göre hemşirelik bakımı uygulamalarında robot hemşirelerin yapmasına izin verdikleri uygulamaların karşılaştırması incelendiğinde "Tıbbi cihaz/malzeme kullanmak" uygulaması dışında tüm uygulamalar ile cinsiyet arasında anlamlı fark olduğu bulunmuştur (p<0.05). Araştırmaya katılan kadın hastaların %95,4'ü boy ve kilo ölçümü, %92.6'sı yatak çarşaflarının değiştirmesi ve %89,8' yaşam bulgularının ölçülmesinde robotların görev almasına yönelik olumlu görüşleri erkeklerin görüşlerine karşılık anlamlı düzeyde fark olduğu bulunmuştur (p<0.05). Araştırmaya katılan erkeklerin %83,9'u emosyonel destek sağlamak, %78,8'i kan almak ve %76,3'ü IV ilaç uygulamasında robotların görev almasına yönelik olumsuz görüşleri kadın hastaların görüşleri ile karşılaştırıldığında istatiksel olarak anlamlı fark olduğu bulunmuştur (p<0.05). Araştırmaya katılan hastaların %60.6'sı yapay zeka ve robot hemşire kavramlarını daha önce hiç duymadıklarını, %72.1'i yapay zekaya sahip robotların hemşirelerin yerini alacağını düşünmediklerini ve %89.8'i yapay zeka ve robot hemşire uygulamalarının hemşirenin iş yükünü azaltacağını düşündüğünü bildirmiştir. Çalışmanın sonuçları hastaların yapay zekâ ve robotik hemşirelik uygulamalarına yönelik tutumlarını inceleyerek literatüre katkı sağlamıştır.This study is a descriptive and cross-sectional research conducted to examine the attitudes of patients hospitalized in internal medicine units at İzmir Bakırçay University Çiğli Training and Research Hospital toward artificial intelligence (AI) and robotic nursing practices. The study was carried out between December 2023 and December 2024 with a total of 226 patients. Data were collected using a "Demographic Information Form" consisting of four questions to determine demographic characteristics, and a "Robot Nurses Questionnaire" consisting of 35 items to assess attitudes toward AI and robotic nursing practices. Data were analyzed using IBM SPSS Statistics 25.0. Descriptive statistical methods (frequency, percentage, minimum-maximum values, mean, standard deviation) and the chi-square test were used to evaluate the data. The Fisher's exact test was utilized to identify the source of differences between groups. A p-value of <0.05 was considered statistically significant. The findings indicated that, except for the item "Using medical devices/equipment," all other nursing interventions showed statistically significant differences by gender regarding patients' willingness to accept robot nurses (p<0.05). Among female participants, 95.4% expressed positive attitudes toward robot nurses performing height and weight measurements, 92.6% supported robots changing bed linens, and 89.8% approved of robots measuring vital signs — all significantly higher than male participants (p<0.05). Conversely, 83.9% of male participants reported negative attitudes toward robot nurses providing emotional support, 78.8% toward blood collection, and 76.3% toward IV medication administration — with statistically significant differences compared to female participants (p<0.05). Furthermore, 60.6% of patients reported that they had never heard of AI and robot nurses before, 72.1% did not believe that AI-powered robots would replace nurses, and 89.8% believed that AI and robot nurse implementations would reduce nurses' workload. The study contributes to the literature by exploring patients' attitudes toward artificial intelligence and robotic nursing practices
Nurse's Opinions on School-Hospital Cooperation: Scale Development Study
No full textBackground and Purpose: This study aims to develop a valid and reliable measurement tool that can evaluate the views of clinical nurses on school-hospital cooperation and the scale of nurses. Methods: Within the scope of validity analyses for the development of the scale, content validity index, construct validity, and known group validity were used. Standard error, Cronbach's alpha, item-total score correlation, and scale response bias methods were used within the scope of reliability analyses. Results: In the final version of the developed scale, the Kaiser-Meyer-Olkin value was 0.90, and Bartlett's test result was chi(2): 2819.610, p < 0.001. It was determined that the total variance of the scale was 45.33%, and the Cronbach's alpha was 0.857. In the scale response bias analysis, the Hotelling T-2 value was 3585.645. Conclusions: It was found that the Nurses' Views of School-Hospital Cooperation Scale is a valid and reliable measurement tool
Evaluation of Caffeine Citrate Prescription in Neonatal Intensive Care Units: National Survey Study
No full text[Abstract Not Available
Minimal Clinically Important Difference for Postural Assessment Scale for Stroke Patients (PASS) and Trunk Impairment Scale (TIS) in Persons With Stroke
No full textObjective: To determine the minimum clinically important difference (MCID) values for the Postural Assessment Scale for Stroke Patients (PASS) and Trunk Impairment Scale (TIS) in persons with stroke. Design: A pre-post interventional study using anchor-based methods. Setting: Inpatient rehabilitation unit. Participants: Sixty-five (N=65) persons with stroke. Interventions: Twenty-session conventional physiotherapy program. Main Outcome Measures: The PASS, TIS, and Functional Independence Measure before and after the physiotherapy program. As the anchor measure, both patients and physiotherapists provided a Global Rating of Change scale score to reflect their perceived changes in postural and trunk control after the intervention. Results: For the PASS, the MCID was 3.5, based on the patient as the anchor, with an area under the curve (AUC) of 0.89 (95% confidence interval [CI], 0.82-0.97). For the physiotherapist ratings, the MCID was 4.5, with an AUC of 0.94 (95% CI, 0.88-0.99). The MCID value was 8.5 for TIS, based on both the patient and physiotherapist as anchors, with AUCs of 0.97 (95% CI, 0.93-1.0) and 0.98 (95% CI, 0.95-1.0), respectively. There were strong correlations (?>0.70, P<.001) between the PASS, TIS, and global rating of change for both patients and physiotherapists, along with functional independence measure. Conclusions: The suggested MCID values for the PASS and TIS are 4.5 and 8.5, respectively, serving as benchmarks to evaluate the effectiveness of physiotherapy treatment in persons with stroke. These MCID values offer meaningful thresholds for identifying clinically important improvements in postural stability and trunk impairment, guiding therapeutic interventions and enhancing treatment evaluation in stroke rehabilitation. © 2025 Elsevier B.V., All rights reserved.İzmir Kâtip Çelebi University, IKCU, (2023-TYL-SABE-0009); İzmir Kâtip Çelebi University, IKC
Search for vector-like leptons with long-lived particle decays in the CMS muon system in proton-proton collisions at = 13 TeV
No full textA first search is presented for vector-like leptons (VLLs) exclusively decaying into a light long-lived pseudoscalar boson and a standard model ? lepton. The pseudoscalar boson is assumed to have a mass below the ?+?? threshold, so that it decays exclusively into two photons. It is identified using the CMS muon system. The analysis is carried out using a data set of proton-proton collisions at a center-of-mass energy of 13 TeV collected by the CMS experiment in 2016–2018, corresponding to an integrated luminosity of 138 fb?1. Selected events contain at least one pseudoscalar boson decaying electromagnetically in the muon system and at least one hadronically decaying ? lepton. No significant excess of data events is observed compared to the background expectation. Upper limits are set at 95% confidence level on the vector-like lepton production cross section as a function of the VLL mass and the pseudoscalar boson mean proper decay length. The observed and expected exclusion ranges of the VLL mass extend up to 700 and 670 GeV, respectively, depending on the pseudoscalar boson lifetime. © 2025 Elsevier B.V., All rights reserved
An Analysis of the The Age of Stupid Documentary in the Context of Environmental Communication
No full textIn the post-1980 period, when industrialization accelerated, the studies in the field of environmental communication gained importance in order to raise awareness with the increase in environmental degradation. Increasing this awareness, the widespread use of mass media with developing technology has been effective. The Age of Stupid is one of the important films in terms of the creation of environmental awareness. Within the scope of this article, the film was examined with qualitative content analysis. In the findings section, 6 real story and archive videos shown in the film are examined through three environmental discourse. These are ecosystem discourse, environmental justice discourse and ecosocialism. It is emphasized that nature, which is deteriorated by the human hand emphasized until the end of the film, reached a dangerous dimension in 2055 due to climate change and global warming. As a result, it has been shown that rich companies and states that have power in The Age of Stupid will be faced with a humanity and nature that will disappear if they continue to exploit nature and poor countries without taking any precautions
In hearing aid applications with hearing aids own fitting application effect of real ear measurement (REM) on speaking discrimination score
No full textİşitme kaybı tedavisinde kullanılan yöntemlerden biri işitme cihazıdır. Literatürde yapılan çalışmalar işitme cihazlarının kullanıcılar tarafından doğru ayarlanmasının konuşmayı anlama becerisini arttırdığını göstermektedir. İşitme cihazlarının kendi fitting uygulamaları mevcuttur, literatürde Gerçek Kulak Ölçümü (REM) ile yapılan uygulamaların kullanıcı memnuniyetini arttığı görülmüştür ancak işitme cihazlarının kendi fitting uygulamalarının ve gerçek kulak ölçümünün (REM) konuşmayı ayırt etme becerisi üzerinde spesifik etkilerini inceleyen çalışmalar sınırlıdır. Bu çalışma, bu belirli ilişkiyi daha derinlemesine anlamak ve işitme cihazlarının uygulama tabanlı ayarlarının, gerçek kulak ölçümü ile birlikte konuşma ayırt etme becerisini nasıl etkilediğini araştırılmıştır. Araştırmaya 35-65 yaş arası 30 bilateral işitme cihazı kullanan Hafif-Orta-İleri dereceli işitme kaybı olan bireyler dahil olmuştur. Katılımcıların mevcut işitme eşikleri değerlendirildikten sonra ilk olarak işitme cihazının uygulaması ile fitting yapılmış ve konuşmayı ayırt etme skorlarına bakılmıştır daha sonra Gerçek Kulak Ölçümü İle (Real Ear Measurement (REM)) işitme cihazı yeniden programlanıp konuşmayı ayırt etme testi tekrar yapılmıştır. İşitme cihazının kendi fitting yöntemi ile programlanması sonrasında yapılan konuşmayı ayırt etme skorları (X?±Ss: 75,20±13,71), REM yöntemi ile işitme cihazı fitting sonrası ölçülen konuşmayı ayırt etme skoru ile (X?±Ss: 82,80±10,92) karşılaştırılmıştır. Bağımlı örneklem t-testi sonuçlarına göre, iki yöntem arasındaki farkın istatistiksel olarak anlamlı olduğu belirlenmiştir (t=-10,46; sd=29; p<0,05). Bu sonuç, REM yöntemiyle yapılan fitting işleminin konuşmayı ayırt etme performansı üzerinde daha olumlu bir etkisi olduğunu göstermektedir.Hearing aid is the one of the using method for the hearing losstreatment. Studies in literature show that the accurate arrangement of hearing aids from users increases discrimination and talking skill. Hearing aids have their fitting application but practices which are made with real ear measurement (REM) show that increase users pleasure yet hearing aids own fitting application and real ear measurement (REM) are limited about speaking discrimination skill,specific effect's examining studies. This study research to understand more deeply, this certain relation and hearing aid's application based settings with real ear measuring (REM) how effective the speaking discrimination score. Between 35-65 years old,30 bilateral hearing aid users,who has light-mid-further gradual hearing loss,individuals are included in the research. After the participations available hearing threshold is considered, first "fitting" made with hearing aids own application and speech discrimination scores were evaluated and afterwards with Real Ear Measurement (REM) hearing aids were reprogrammed and speech discrimination test remake. After being programmed with the hearing aids' own fitting, the measured speaking discrimination test scores (X? ±Ss: 75,20±13,71) were compared with those obtained using REM hearing aid fitting (X? ±Ss: 82,80±10,92). According to the dependent sample t test differences between Two methods have been significantly statically determinate (t=-10,46; sd=29; p<0,05). This result shows thatmaking "fitting" operation with REM method has a more positive effect on speaking discrimination performance