Tag: statistics

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

Environ Sci Technol. 2026 Apr 22. doi: 10.1021/acs.est.6c03813. Online ahead of print.

ABSTRACT

Temperature regulates mollusk physiology and can alter metal bioaccumulation through filtration, uptake, growth dilution, and elimination. Yet many toxicokinetic (TK) applications treat temperature as a simple correction to a subset of rates and rarely account for trait- and context-dependence, limiting transferability across studies, seasons, and warming scenarios. Here we synthesize experimental evidence for marine univalve and bivalve mollusks and develop a temperature-aware framework that couples one-compartment mass-balance TK with temperature-dependent filtration and growth, while statistically linking absorption efficiency and elimination to temperature, species traits (e.g., body size), and metal chemical properties. Thermal responses in filtration and growth were captured with unimodal performance functions; machine learning was used for predictor screening. Evaluated against an independent data set, the framework reproduced internal concentrations across multiple orders of magnitude with good agreement (R² ≈ 0.73). Across the compiled evidence, filtration and growth showed strong species-specific thermal sensitivity, while metal chemistry primarily structured uptake. In the limited multitemperature TK calibration data set, a positive association between temperature and elimination was observed, but this relationship should be regarded as provisional pending additional multitemperature uptake-depuration data sets. By explicitly representing temperature-sensitive filtration and turnover pathways, the approach enables scenario testing for warming and heat extremes and provides a practical basis for improving the interpretation of bioaccumulation factors, seasonal biomonitoring, and temperature-aware risk assessment under climate change.

PMID:42019011 | DOI:10.1021/acs.est.6c03813

JMIR Mhealth Uhealth. 2026 Apr 22;14:e86836. doi: 10.2196/86836.

ABSTRACT

BACKGROUND: Cancer poses a significant threat to children’s health, and mobile health (mHealth) is emerging as a key tool for remote disease management, health education, and follow-up. However, evidence of its effectiveness remains limited.

OBJECTIVE: This study aimed to summarize the effects of mHealth interventions for pediatric cancer compared with usual care, providing evidence-based support for optimizing intervention models and improving patient outcomes.

METHODS: A systematic search of 14 databases identified randomized controlled trials (RCTs) on mHealth apps for pediatric patients with cancer from inception to August 1, 2025. Two reviewers independently screened studies, extracted data, assessed bias risk, and graded evidence quality. The meta-analysis was conducted using RevMan 5.4 and Stata 15.

RESULTS: A total of 24 RCTs involving 2645 patients were included. This review found that mHealth interventions significantly reduced infection rates (odds ratio [OR] 0.25, 95% CI 0.10-0.60; P=.002) and the overall incidence of peripherally inserted central catheter (PICC) complications (OR 0.16, 95% CI 0.10-0.24; P<.001), while improving quality of life (standardized mean difference [SMD] 1.34, 95% CI 0.13-2.55; P=.03), self-management ability (SMD 6.39, 95% CI 1.26-11.53; P=.01), and treatment adherence (OR 2.83, 95% CI 1.41-5.66; P=.003). However, mHealth interventions had no significant effect on PICC catheter displacement (OR 0.44, 95% CI 0.15-1.29; P=.13) or health knowledge (SMD 4.44, 95% CI -2.40 to 11.29; P=.20). Further high-quality studies are needed to verify their impact in these areas. The intervention components covered 9 behavior change techniques: goals and planning, feedback and monitoring, social support, shaping knowledge, repetition and substitution, reward and threat, comparison of outcomes, natural consequences, and regulation.

CONCLUSIONS: This systematic review and meta-analysis synthesized evidence from RCTs. The findings support the use of mHealth to reduce infections and PICC-related complications among pediatric patients with cancer while improving quality of life, self-management capabilities, and treatment adherence. These results underscore the importance of incorporating mHealth strategies into pediatric cancer care and guide the development and enhancement of future mHealth interventions.

PMID:42018994 | DOI:10.2196/86836

JMIR Cancer. 2026 Apr 22;12:e81579. doi: 10.2196/81579.

ABSTRACT

BACKGROUND: Fear of cancer recurrence (FCR) is prevalent among cancer survivors, affecting between 39% and 97% of patients. FCR is associated with impaired concentration, sleep disturbances, decreased quality of life, and increased psychological distress and health care use. To date, the literature lacks a review that summarizes the breadth of psychological interventions available for reducing fear of recurrence.

OBJECTIVE: This review aims to identify and summarize the evidence on psychological interventions for addressing FCR across all cancers.

METHODS: The Joanna Briggs Institute method for scoping reviews guided the processes, and we reported the review following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. We searched 5 databases (CINAHL, PsycInfo, the Cochrane Central Register of Controlled Trials, Embase, and MEDLINE) and 2 gray literature sources (ProQuest Dissertations & Theses Global and the World Health Organization International Clinical Trials Registry). Eligible studies included adults (≥18 years) diagnosed with cancer and evaluated psychological interventions aimed at reducing FCR. Data extraction captured study characteristics, intervention details, outcome effectiveness, and follow-up durations. We synthesized the findings using descriptive summaries and narrative analysis.

RESULTS: Overall, 5131 articles were screened, and 122 were included in this review; 48 (39.3%) involved patients with breast cancer, 47 (38.5%) focused on patients with multiple cancer types; over half of the studies (n=64, 52.5%) were randomized controlled trials. Only 28 (23%) studies explicitly reported the definition of FCR. Eighteen different measurement tools were used. Blended interventions (different combinations of cognitive behavioral therapy, mindfulness, acceptance and commitment therapy, and other strategies) formed the largest intervention category (n=38, 31.1%), followed by cognitive behavioral therapy interventions (n=26, 21.3%) and mindfulness-based interventions (n=24, 19.7%). Of the included studies, 104 (85.2%) demonstrated significant reductions in FCR. Most interventions were delivered face-to-face by disciplinary specialists (n=75, 61.5%), while some were delivered remotely (n=34, 27.9%), with the majority of these delivered via the website (n=18, 52.9%). Follow-up duration ranged from postintervention to 3 years.

CONCLUSIONS: FCR has been the focus of an increasing number of studies since 2009, with the majority being randomized controlled trials. Most interventions are delivered face-to-face and rely on trained specialists. Most have had statistically significant results. However, the included studies demonstrated heterogeneity in terms of delivery, duration, and dose, requiring cautious interpretation of intervention effects. Future research should develop consistent guidelines to standardize the definition of FCR, the measurement tools used, and the timing of follow-up assessments. Long-term follow-up data are needed to evaluate the sustained effects.

PMID:42018993 | DOI:10.2196/81579

JMIR AI. 2026 Apr 22;5:e78764. doi: 10.2196/78764.

ABSTRACT

BACKGROUND: In recent years, there has been increasing interest in developing machine and deep learning models capable of annotating clinical documents with semantically relevant labels. However, the complex nature of these models often leads to significant challenges regarding interpretability and transparency.

OBJECTIVE: This study aims to improve the interpretability of transformer models and evaluate the explainability of a deep learning-based annotation of coded clinical documents derived from death certificates. Specifically, the focus is on interpreting and explaining model behavior and predictions by leveraging calibrated confidence, saliency maps, and measures of instance difficulty applied to textualized representations coded using the International Statistical Classification of Diseases and Related Health Problems (ICD). In particular, the instance difficulty approach has previously proven effective in interpreting image-based models.

METHODS: We used disease language bidirectional encoder representations from transformers, a domain-specific bidirectional encoder representations from transformers model pretrained on ICD classification-related data, to analyze reverse-coded representations of death certificates from the US National Center for Health Statistics, covering the years 2014 to 2017 and comprising 12,919,268 records. The model inputs consist of textualized representations of ICD-coded fields derived from death certificates, obtained by mapping codes to the corresponding ICD concept titles. For this study, we extracted a subset of 400,000 certificates for training, 100,000 for testing, and 10,000 for validation. We assessed the model’s calibration and applied a temperature scaling post-hoc calibration method to improve the reliability of its confidence scores. Additionally, we introduced mechanisms to rank instances by difficulty using Variance of Gradients scores, which also facilitate the detection of out-of-distribution cases. Saliency maps were also used to enhance interpretability by highlighting which tokens in the input text most influenced the model’s predictions.

RESULTS: Experimental results on a pre-fine-tuned model for predicting the underlying cause of death from reverse-coded death certificate representations, which already achieves high accuracy (0.990), show good out-of-the-box calibration with respect to expected calibration error (1.40), though less so for maximum calibration error (30.91). Temperature scaling further reduces expected calibration error (1.13) while significantly increasing maximum calibration error (42.17). We report detailed Variance of Gradients analyses at the ICD category and chapter levels, including distributions of target and input categories, and provide word-level attributions using Integrated Gradients for both correctly classified and failure cases.

CONCLUSIONS: This study demonstrates that enhancing interpretability and explainability in deep learning models can improve their practical utility in clinical document annotation. By addressing reliability and transparency, the proposed approaches support more informed and trustworthy application of machine learning in mission-critical medical settings. The results also highlight the ongoing need to address data limitations and ensure robust performance, especially for rare or complex cases.

PMID:42018992 | DOI:10.2196/78764

JMIR Res Protoc. 2026 Apr 22;15:e83225. doi: 10.2196/83225.

ABSTRACT

BACKGROUND: Research is needed to better understand mental health (MH) problems among higher education (HE) students and how to address them. The Observatory on Student Mental Health in Higher Education (OSMHHE) brings together 350 members across Quebec (Canada) and internationally. Its mission is to develop, promote, and disseminate knowledge to foster a culture that supports student MH in HE.

OBJECTIVE: This study aims to describe the OSMHHE’s research protocol, which consists of three objectives: (1) establishing a portrait of students’ MH and its determinants, (2) identifying and evaluating a variety of MH practices, and (3) assessing the implementation and impacts of the OSMHHE as a knowledge mobilization infrastructure.

METHODS: Objective 1 will be achieved through a provincial survey using a cross-sectional, repeated-measures design with 2 data collections (November 2024 and 2026) targeting the entire Quebec HE student population. Dimensions, indicators, and scales were selected based on conceptual frameworks, a systematic literature review, and Delphi methods. Analyses will include descriptive statistics by education levels; inferential analyses comparing subpopulations; multiple regressions, logistic models, and linear mixed models to identify MH determinants; and repeated-measures ANOVA to examine temporal changes. Objective 2 will evaluate the implementation, sustainability, scale-up, and impacts of MH practices using mixed methods. Analyses may include descriptive and comparative statistics, correlations, structural equations modeling (path analysis), and qualitative content or thematic analyses. Objective 3 will draw on the framework of Ziam et al to assess knowledge mobilization strategies. A developmental evaluation approach and convergent mixed methods design within a case study will be used to assess the OSMHHE’s implementation and impacts. Qualitative data will include semistructured individual and group interviews with OSMHHE members, addressing topics such as roles, decision-making processes, facilitators and barriers, and outcomes. Additional qualitative sources will include diverse documents (eg, meeting agendas, reports). Quantitative data will come from questionnaires completed by members examining levels of engagement and satisfaction, challenges and barriers, and impacts of the OSMHHE’s activities and knowledge mobilization practices. Qualitative data will be analyzed using content analyses. Quantitative data will be examined using descriptive, comparative, and correlational analyses.

RESULTS: This project is funded from February 2023 to February 2028. The first provincial survey took place in November 2024, collecting data from 32,212 students in 77 HE institutions. Analyses are underway, and a first report was released in November 2025. Approximately 20 student MH practices are currently being evaluated.

CONCLUSIONS: The OSMHHE provincial survey will provide portraits of students’ MH in HE in Quebec and its determinants to better guide MH practices and institutional decision-making. Evaluating MH practices will advance knowledge of their effectiveness. Assessing the implementation of the OSMHHE will help deepen our understanding of knowledge mobilization infrastructures designed to support student MH in HE.

PMID:42018986 | DOI:10.2196/83225

JMIR Hum Factors. 2026 Apr 22;13:e87846. doi: 10.2196/87846.

ABSTRACT

BACKGROUND: Near miss events can reveal system problems before patients are harmed, but current reviews are inconsistent and often rely on simple counts that are distorted by patient volume and reporting culture. Consequently, leaders cannot tell whether a rise in reports means that safety is getting worse or that staff are reporting more, and current systems are not strong enough to clearly separate real safety risks from random variation.

OBJECTIVE: This study developed a 3-level near miss framework (NM³), a conceptual framework that converts descriptive near miss data into decision-grade intelligence through a structured, evidence-based process, including baseline measurement and advanced interpretation and governance.

METHODS: NM³ was developed to provide decision-grade analytics for acute inpatient hospital settings. The framework was designed as a maturity model, progressing from baseline measurement to advanced interpretation. It integrates standardized definitions, rate calculations, statistical process control, severity weighting, and learning metrics.

RESULTS: Level 1 establishes an organizational baseline through near miss rates per 1000 patient-days and near miss-to-harm ratios monitored with control charts. Level 2 introduces domain-specific denominators and unit-level charts to detect local variation. Level 3 applies severity weighting to generate a Near Miss Index; incorporates learning yields at 90 and 180 days; and triangulates near miss trends with harm events, exposure, reporting volume, and culture measures. A synthetic example demonstrates how the framework converts raw reports into stable rates, weighted indices, and learning metrics.

CONCLUSIONS: NM³ provides a structured pathway for organizations to strengthen near miss analytics. By progressing through maturity levels, leaders can improve the interpretation of safety signals, prioritize high-consequence risks, and integrate near miss reporting into governance.

PMID:42018985 | DOI:10.2196/87846