Tag: pubmed

Environ Monit Assess. 2026 Apr 22;198(5):490. doi: 10.1007/s10661-026-15354-6.

ABSTRACT

China confronts dual environmental challenges: PM_2.5-O₃ synergistic pollution and mounting CO₂ emissions, demanding a strategic shift from single-pollutant control to coordinated management. In response, this study develops a trivariate synergy index (TSI) based on the bivariate synergy index (BSI), integrating PM_2.5, O₃, and CO₂ into a single metric to quantify differences in synergy levels across regions. Using high-resolution remote sensing and 2023 statistical data, we apply an XGBoost-SHAP modeling framework to analyze the seasonal spatio-temporal patterns and drivers of the TSI in 82 key Chinese cities. The results revealed that (1) the BSI_PM2.5-O3 was positive only in summer (mean = 0.03), with better conditions along the eastern coast. BSI_PM2.5-CO2 was most negative in summer (mean = -2.74), while BSI_O3-CO2 shifted from negative in summer to positive in autumn, showing a latitudinal gradient with higher values in the north. (2) Summer TSI showed the weakest antagonism (mean = -1.37) among the four seasons, with values increasing northward and decreasing eastward. Spatially, coastal Bohai regions shifted from positive in spring to negative in summer, while the area surrounding Jiangsu exhibited persistent negative values. (3) The core factors driving TSI variation across all four seasons are temperature, precipitation, and elevation, with their effects exhibiting nonlinear threshold effects. Among socioeconomic factors, GDP exerted a positive influence only after surpassing a critical threshold, whereas increased population density had a negative regulatory effect. Based on the findings, this study offers a seasonal, spatially differentiated reference for synergistic pollution and carbon reduction.

PMID:42020634 | DOI:10.1007/s10661-026-15354-6

Eur Radiol. 2026 Apr 23. doi: 10.1007/s00330-026-12563-y. Online ahead of print.

NO ABSTRACT

PMID:42020625 | DOI:10.1007/s00330-026-12563-y

Eur Radiol. 2026 Apr 23. doi: 10.1007/s00330-026-12543-2. Online ahead of print.

ABSTRACT

OBJECTIVE: To systematically evaluate training sample size adequacy in externally validated machine learning (ML)-based radiomics models published in high-impact journals and quantify the gap between current practice and theoretical minimum requirements.

MATERIALS AND METHODS: This study followed a prespecified and publicly archived protocol. Original research articles published between January 2023 and August 2025 in first quartile (Q1) journals were evaluated. Study selection followed a randomized dynamic screening protocol with a priori power-calculated stopping rule to determine the final cohort. Included studies developed binary prediction models using ML algorithms other than logistic regression and reported external validation. A sample size framework, originally developed for logistic regression, was applied as a conservative lower-bound benchmark. Minimum required sample sizes were calculated based on reported training performance, outcome prevalence, and feature dimensionality.

RESULTS: Of 64 full-text records assessed, 16 (25%) were unassessable due to missing essential parameters (e.g., feature counts) required for sample size estimation. In the assessable final cohort (n = 28), the training sample sizes observed were consistently inadequate, with a median deficit of 195.5 training instances. Only three studies (10.7%) met all criteria for stable prediction model development even under these charitable assumptions. Most studies failed basic heuristics (e.g., 10 events per predictor), with a median events per predictor deficit of 5.8.

CONCLUSION: The vast majority of externally validated radiomics models in high-impact journals are trained on datasets statistically insufficient to support their algorithmic complexity. This systemic data deficit renders models prone to overfitting and instability, potentially explaining the field’s reproducibility crisis.

KEY POINTS: Question Do externally validated machine learning-based radiomics models in high-impact (Q1) journals have sufficient training sample sizes to support their reported model complexity? Findings Nearly 90% of externally validated radiomics models were trained on statistically insufficient datasets, with a median deficit of approximately 200 training instances. Clinical relevance Insufficient training sample sizes undermine model stability and contribute to the reproducibility crisis in radiomics, allowing externally validated models to appear robust while generating unreliable predictions that may misinform clinical decision-making.

PMID:42020624 | DOI:10.1007/s00330-026-12543-2

Eur Radiol. 2026 Apr 22. doi: 10.1007/s00330-026-12555-y. Online ahead of print.

ABSTRACT

OBJECTIVE: Accurate morphometric measurements are crucial for musculoskeletal radiography, but they remain labor-intensive and prone to inter-reader variability. Current artificial intelligence-based solutions often require large annotated training datasets and narrow applications. We present and validate a training-free artificial intelligence framework that automatically derives morphometric measurements across multiple anatomies and radiographic views using universal landmark matching.

MATERIALS AND METHODS: In this retrospective study, 600 standard radiographs of the foot, knee, and shoulder are analyzed. Additionally, a cohort of 240 challenging radiographs containing orthopedic implants was constructed to stress-test the approach. Landmarks from reference radiographs are transferred to unseen radiographs using a pre-trained generalist dense-matching method, and are then used to derive measurements in a post-processing step. The resulting measurements were compared with manual annotations and measurements by two radiologists.

RESULTS: Mean landmark matching error is 2.68 ± 2.70 mm using a single reference radiograph and improves to 2.15 ± 2.38 mm with 40 reference radiographs. Measurement accuracy ranges from 1.81° (I-II metatarsal angle) to 8.65° (congruence angle). Increasing the number of reference images improved measurement accuracy, and mostly approached inter-reader agreement. Performance is mixed on the challenging cohort, demonstrating the limitations and strengths of the approach.

CONCLUSIONS: This anatomy-agnostic framework enables training-free morphometry across multiple regions, with measurement-dependent performance often comparable to inter-reader agreement. Challenging cases highlight specific limitations, motivating the use of quality control and reference-set tuning for deployment. Its minimal setup enables rapid adaptation to new anatomies and measurements, and clinically practical runtimes require GPU inference.

KEY POINTS: Question Can a generalist artificial intelligence framework be used to accurately and automatically perform morphometric measurements across different musculoskeletal radiographs without anatomy-specific training? Findings The training-free approach achieved performance that approaches expert-level agreement for most measurements, while highlighting measurement-specific limitations in challenging cases. Multiple reference radiographs improved results. Clinical relevance This approach automates repetitive morphometric measurements that are prone to inter-reader variability, reducing manual workload while providing reproducible results that can approach expert radiologist performance. Its adaptability and minimal setup enable integration into routine workflows.

PMID:42020623 | DOI:10.1007/s00330-026-12555-y

Eur Radiol. 2026 Apr 22. doi: 10.1007/s00330-026-12554-z. Online ahead of print.

ABSTRACT

OBJECTIVES: To systematically review the available evidence on electrochemotherapy (ECT) in pediatric patients, focusing on indications, treatment parameters, efficacy, and safety.

MATERIALS AND METHODS: A systematic review was conducted in accordance with PRISMA guidelines and registered with PROSPERO (CRD42024575588). MEDLINE, Embase, and Cochrane Library databases were searched. Studies reporting the use of ECT in patients ≤ 18 years were included. Data extraction covered patient demographics, pathologies, electroporation parameters, agents used, and outcomes. Risk of bias was assessed using ROBINS-I.

RESULTS: Out of 1579 screened studies, 15 met the inclusion criteria, reporting at least 127 pediatric patients. Age was provided for 98 patients (pooled mean 8.5 years, range 0-17) and sex for 108 (58 female, 50 male). Two studies described tumors; the remainder reported vascular anomalies (VAs). Reversible electroporation with intralesional or intravenous bleomycin was the most common protocol. Complete response rates for tumors were 97-100%. Pooled volume reductions in VAs ranged from 52% to 100%. Most complications were minor, though serious events occurred, including sciatic nerve injury, disseminated intravascular coagulation, and airway compromise. Methodological quality was low, with small sample sizes and inconsistent reporting of multiple parameters.

CONCLUSION: ECT is in the early stages of development but shows promising efficacy as a treatment for tumors and VAs in children. Preliminary data suggest favorable responses in children, although direct comparison with adults is limited. Further research is essential to establish standardized treatment guidelines, optimize safety, and define the role of ECT within pediatric oncology and interventional radiology. A minimum reporting dataset is proposed.

KEY POINTS: Question Explain the unmet need/clinical problem your study addresses. What is the current evidence regarding indications, efficacy, safety, and treatment parameters of ECT in pediatric patients? Findings Fifteen studies, including 127 children, report high response rates for tumors and VAs, but evidence is limited by small cohorts and heterogeneous reporting. Clinical relevance ECT shows promise as a minimally invasive treatment for selected pediatric tumors and VAs, but standardized protocols and large prospective studies are required before broader clinical adoption in pediatric oncology and interventional radiology.

PMID:42020622 | DOI:10.1007/s00330-026-12554-z

Eur Radiol. 2026 Apr 22. doi: 10.1007/s00330-026-12568-7. Online ahead of print.

NO ABSTRACT

PMID:42020621 | DOI:10.1007/s00330-026-12568-7

Reprod Biomed Online. 2026 Feb 18;52(6):105632. doi: 10.1016/j.rbmo.2026.105632. Online ahead of print.

ABSTRACT

Among the many symbols that appear in the medical literature, few have received as much attention as the letter ‘P’. It is printed in almost every table, discussed in every result section and often treated as the final word in the interpretation of scientific findings. In reality, the P-value is neither over-rated nor under-rated. It is simply misunderstood. It is an important tool, but it is not the main factor that determines the value of a study. What matters is not the number itself, but how we think about it and what it truly represents. This manuscript revisits the ‘value’ of the P-value in reproductive medicine.

PMID:42019099 | DOI:10.1016/j.rbmo.2026.105632

Nurse Educ Today. 2026 Apr 21;164:107125. doi: 10.1016/j.nedt.2026.107125. Online ahead of print.

ABSTRACT

BACKGROUND: Despite extensive efforts, pressure injuries (PIs) remain a critical concern in healthcare quality. Consequently, robust training in PI prevention, assessment and management-tailored to the learning needs of new generations-is imperative for nursing undergraduates to ensure safe, effective and person-centred care delivery.

AIM: To design, implement and evaluate an immersive virtual reality (IVR) training program for PI care, comparing its effects on nursing students’ reflective thinking (RT) and clinical competence against conventional teaching approaches.

DESIGN: Prospective, randomized, double-blind, parallel-group controlled trial.

SETTING: This study was conducted at a university in northern Spain.

PARTICIPANTS: The study convenience sample comprised 93 second-year nursing students. The majority were female (93.4%) with a mean age of 19.3 years. Importantly, 63.4% reported no prior experience with IVR.

METHODS: Six PI nursing care scenarios were designed and developed for IVR using head-mounted displays (Oculus Quest 2), in accordance with internationally recognized standards and evidence-based clinical guidelines. Key variables measured included RT capacity (using Gibbs cycle), knowledge gain, skills performance and usability and satisfaction. Data analysis involved descriptive and parametric statistics (Student’s t-test) and covariance methods to compare outcomes and assess the impact between groups, using SAS v. 9.4.

RESULTS: The intervention group (IVR = 47 students) demonstrated statistically significant improvement in RT compared to the control group (46 students), particularly in the “Emotion” and “Conclusion” questions of Gibbs’ cycle. Skills gain was also significantly higher in the IVR group (p < 0.001). While knowledge gains were comparable (p = 0.202) -indicating no additional advantage of IVR over traditional methods in this specific domain-, the IVR group reported higher satisfaction and usability levels.

CONCLUSIONS: IVR-based applications effectively enhance nursing students’ RT and skills in PI care. This technology offers a valuable educational tool for improving student competence, especially for those with lower initial skill levels, and can be considered an innovative alternative to traditional teaching methods.

PMID:42019094 | DOI:10.1016/j.nedt.2026.107125

JMIR Res Protoc. 2026 Apr 22;15:e76945. doi: 10.2196/76945.

ABSTRACT

BACKGROUND: Accreditation programs are used by hospitals and health services to be independently reviewed against established safety and quality standards and have been a feature of global health systems for over a century. While evidence that accreditation programs directly improve patient safety and quality outcomes exists, the findings of various researchers remain mixed. Inefficiencies and a culture of “gaming” the system have also been observed, raising questions about the overall effectiveness of accreditation programs and assessment processes. Consequently, exploration of other formats of accreditation assessment, such as short-notice accreditation assessment, has arisen. From July 1, 2023, the Australian Commission on Safety and Quality in Healthcare mandated that Australian public and private hospitals must engage in short-notice accreditation assessment.

OBJECTIVE: This study aims to explore the impact of short-notice accreditation assessment on hospitals, both in terms of safety and quality indicators, and organizational culture. A mixed methods design will be used to investigate these impacts.

METHODS: Quantitative safety and quality indicators will be drawn from a regional health service prior to and following its first short-notice accreditation assessment cycle. From the same site, staff will be invited to complete the Patient Safety Culture Survey and participate in semistructured interviews. Using Schein’s Culture Framework as an organizational culture model, the study will examine observable outcomes (artifacts, behaviors, and indicators) alongside staff perceptions and experiences (norms and values) to form an understanding of underlying assumptions and beliefs about short-notice accreditation assessment processes. Quantitative data will be analyzed through cross-tabulation, trend analysis, and other statistical techniques, while qualitative data will be synthesized to provide a comprehensive understanding.

RESULTS: This protocol outlines the planned evaluation of short-notice accreditation assessment and its influence on patient safety and quality culture within a regional health service. Data collection is underway, with preintervention surveys being completed, and recruitment open for postintervention interviews. The study is expected to generate new knowledge on how this accreditation assessment process affects patient safety and quality culture of a regional and a rural hospital.

CONCLUSIONS: The findings will inform health policy on the suitability and long-term viability of short-notice accreditation assessment as an approach to ensuring safe, high-quality health care.

PMID:42019040 | DOI:10.2196/76945

N Engl J Med. 2026 Apr 23;394(16):1583-1594. doi: 10.1056/NEJMoa2502457.

ABSTRACT

BACKGROUND: Nirmatrelvir-ritonavir has been shown to reduce progression to severe illness from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in unvaccinated high-risk outpatients. The effectiveness of nirmatrelvir-ritonavir in persons who have been vaccinated, infected naturally, or both is unclear.

METHODS: In two open-label platform trials (PANORAMIC in the United Kingdom and CanTreatCOVID in Canada), we enrolled higher-risk adults (≥50 years of age or ≥18 years of age with coexisting conditions) in the community who tested positive for SARS-CoV-2 and had been unwell for 5 days or less. The participants were randomly assigned to receive usual care plus nirmatrelvir (300 mg)-ritonavir (100 mg) twice a day for 5 days or to receive usual care alone. The primary outcome was hospitalization or death from any cause within 28 days after randomization.

RESULTS: From December 8, 2021, to September 30, 2024, a total of 3516 participants in the PANORAMIC trial and 716 participants in the CanTreatCOVID trial underwent randomization. In the PANORAMIC trial, 14 of 1698 participants (0.8%) in the nirmatrelvir-ritonavir group and 11 of 1673 participants (0.7%) in the usual-care group were hospitalized or died (adjusted odds ratio, 1.18; 95% Bayesian credible interval, 0.55 to 2.62; probability of superiority, 0.334). In the CanTreatCOVID trial, 2 of 343 participants (0.6%) in the nirmatrelvir-ritonavir group and 4 of 324 participants (1.2%) in the usual-care group were hospitalized or died (adjusted odds ratio, 0.48; 95% Bayesian credible interval, 0.08 to 2.23; probability of superiority, 0.830). In a substudy involving 634 participants, viral load was reduced by the end of treatment with nirmatrelvir-ritonavir. Serious adverse events with nirmatrelvir-ritonavir were reported in 9 participants in the PANORAMIC trial and in 4 participants in the CanTreatCOVID trial.

CONCLUSIONS: In two open-label trials, nirmatrelvir-ritonavir did not reduce the incidence of hospitalization or death among vaccinated higher-risk participants with SARS-CoV-2 infection. (Funded by the National Institute for Health and Care Research, and others; PANORAMIC ISRCTN number, 2021-005748-31; CanTreatCOVID ClinicalTrials.gov number, NCT05614349.).

PMID:42019019 | DOI:10.1056/NEJMoa2502457