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To understand the social representations of bedside milk expression (BME) among mothers of preterm newborns in neonatal intensive care units (NICUs).
Qualitative descriptive study.
The study was conducted from July to August 2024 in two NICUs of a referral maternity hospital in Fortaleza, Brazil. Nineteen mothers of hospitalised premature newborns participated. Semi-structured interviews were conducted and subjected to thematic content analysis.
Mothers perceived BME as a meaningful act of protection and bonding, though some were unfamiliar with the practice. Emotional ambivalence was common, shaped by prior breastfeeding experiences and the context of prematurity. Discomfort related to privacy and shared spaces was noted. Support from healthcare professionals was essential to promote understanding and adherence.
Social representations of BME are shaped by emotional, social and institutional experiences. Anchored in prior breastfeeding experiences and cultural meanings of maternal care, the practice is objectified through both gestures of affection and tangible barriers.
Healthcare professionals, particularly nurses, should receive training to support mothers in BME. Structural improvements, privacy and emotional support are essential for fostering maternal autonomy and confidence.
This study highlights the barriers to BME, emphasising the role of healthcare support and the need for better infrastructure, privacy and training to enhance maternal confidence and breastfeeding.
The study followed the Consolidated Criteria for Reporting Qualitative Research checklist.
None.
This paper highlights the pivotal role of healthcare professional support in overcoming barriers to BME and promoting breastfeeding practices.
Fresh breast milk is considered the gold standard for reducing complications and improving survival in preterm infants. BME is recommended as an effective strategy to ensure the availability of fresh breast milk. Mothers' social representations of this practice remain underexplored within the neonatal intensive care context.
Explores mothers' social representations of BME in NICUs, addressing a significant gap in qualitative research. Reveals how emotional, social and institutional factors shape mothers' perceptions, motivations and challenges related to BME. Highlights the need for targeted professional support, improved infrastructure and privacy to enhance maternal autonomy and adherence to milk expression practices.
Healthcare professionals, particularly nurses, should receive specialised training to provide technical guidance and emotional support, enhancing mothers' confidence and autonomy in BME. Improving infrastructure and ensuring privacy in NICUs are crucial to creating supportive environments that facilitate milk expression and strengthen maternal–infant bonding. Institutional policies should integrate maternal-centred strategies to support breastfeeding continuity and promote humanised neonatal care.
Postoperative pulmonary complications (PPCs) represent a significant cause of postoperative morbidity and even mortality. However, there is a lack of consensus regarding this composite endpoint, the definition of the individual components, classification and optimal outcome measures. This study aims to refine the PPCs composite framework by evaluating its construct validity, assessing the necessity and risks of a composite measure and exploring the feasibility of differentiating severity categories.
A Delphi consensus process will be conducted, engaging an international multidisciplinary group of 30–40 panellists, including clinicians, researchers, patients, public representatives and health economists. Through iterative rounds, the study will seek agreement on the individual components of the PPCs composite. Additionally, consensus will establish a framework for a composite outcome measure based on a standardised severity classification, appropriate timeframes and weighted grading of PPCs.
Consensus, defined by ≥75% concurrence in multiple choice questions or on Likert–scale statements, will be evaluated from round 2 onwards. Delphi rounds will be continued until all statements have reached stability of responses evaluated by 2 tests or the Kruskal-Wallis test.
The study will be conducted in strict compliance with the principles of the Declaration of Helsinki and will adhere to ACCORD guidance for reporting. Ethics approval has been obtained for this study from the University of Wolverhampton, UK (SOABE/202425/staff/3). Informed consent will be obtained from all panellists before the commencement of the Delphi process. The results of the study will be published in a peer–reviewed journal with the authorship assigned in accordance with ICMJE requirements.
NCT06916598 (clinicaltrials.gov).
Machine Learning (ML) has been transformative in healthcare, enabling more precise diagnostics, personalised treatment regimens and enhanced patient care. In cardiology, ML plays a crucial role in risk prediction and patient stratification, particularly for heart failure (HF), a condition affecting over 64 million people globally and imposing an economic burden of approximately $108 billion annually. ML applications in HF include predictive analytics for risk assessment, identifying patient subgroups with varying prognoses and optimising treatment pathways. By accurately predicting the likelihood of hospitalisation and rehospitalisation, ML tools help tailor interventions, reduce hospital visits, improve patient outcomes and lower healthcare costs.
To conduct a comprehensive review of existing ML models designed to predict hospitalisation risk in individuals with HF.
A database search including PubMed, SCOPUS and Web of Science was conducted on 31 March 2024. Studies were selected based on inclusion criteria focusing on ML models predicting hospitalisation risks in adults with HF. The data from 27 studies meeting the criteria were extracted and analysed, with a focus on the predictive performance of the ML models and the presence of economic analysis.
Most studies focused on predicting readmission rather than first-time hospitalisation. All included studies employed supervised ML algorithms, with ensemble-based methods generally yielding the highest predictive performance. For 30-day hospitalisation or readmission risk, Extreme Gradient Boosting (XGBoost) achieved the highest mean area under the curve (AUC) (0.69), followed by Naïve Bayes (0.68) and Deep Unified Networks (0.66). For 90-day risk, the best-performing models were Least Absolute Shrinkage and Selection Operator and Gradient Boosting, both with a mean AUC of 0.75, followed by Random Forest (0.67). When the prediction timeframe was unspecified, Categorical Boosting achieved the highest performance with a mean AUC of 0.88, followed by Generalised Linear Model Net and XGBoost (both 0.79).
Electronic health records were the primary data source across studies; however, few models included patient-reported outcomes or socioeconomic variables.
None of the studies conducted an economic evaluation to assess the cost-effectiveness of these models.
ML holds substantial potential for improving HF care. However, further efforts are needed to enhance the generalisation of models, integrate diverse data sources and evaluate the cost-effectiveness of these technologies.
by Pedro Tadao Hamamoto Filho, Maria de Lourdes Marmorato Botta Hafner, Zilda Maria Tosta Ribeiro, Alba Regina de Abreu Lima, Leandro Arthur Diehl, Neide Tomimura Costa, Maria Cristina de Andrade, Samira Yarak, Patrícia Moretti Rehder, Júlio César Moriguti, Angélica Maria Bicudo
BackgroundIt has been proposed that the school origin of items for cross-institutional Progress Tests (PTs) may introduce a bias in favour of students from the same school, posing a potential threat to the validity and reliability of PT results and cross-institutional comparisons. The aim of this study was to examine whether origin bias is present in a Brazilian cross-institutional PT examination.
MethodsThis study conducted a cross-sectional analysis of seven schools affiliated with the oldest PT consortium in Brazil, utilising a pooled analysis of differences in students’ performance concerning self and non-self items. A proportional meta-analysis of the items’ rate differences and confidence intervals with random effects was performed, providing an odds ratio (OR) for self and non-self items. Differences between the two groups of items were assessed by scrutinising whether the OR and 95% confidence intervals overlapped.
ResultsThe findings indicated no discernible differences in psychometric indices based on the school responsible for item creation. Three schools consistently demonstrate superior performance on items authored by their faculty, however, these they also excelled on non-self items. Furthermore, an overlap in the 95% confidence intervals for both self and non-self items was observed across all seven schools.
ConclusionsIn contrast to prior reports, this study revealed the absence of origin bias, suggesting that adoption of best practices in blueprinting, item writing, and editing may have played a role in mitigating such bias.
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