Category: Statistics

Phlebology. 2026 Feb 17:2683555261424388. doi: 10.1177/02683555261424388. Online ahead of print.

ABSTRACT

ObjectiveTo compare the clinical efficacy of two postoperative compression methods at 48 h after endovenous radiofrequency ablation (RFA) of the great saphenous vein on complications, quality of life, return-to-work time, and patient satisfaction.MethodsIn this prospective, single-center randomized controlled trial, 210 patients with duplex ultrasound-confirmed great saphenous vein incompetence (C2-C5) underwent RFA and were randomized to receive either elastic stockings (study group) or multilayer bandage compression (control group) for 48 h postoperatively. Primary outcomes included postoperative complications assessed at 2 h, 1 day, 2 days, 7 days, and 1 month. Secondary outcomes included pain (VAS), quality of life (CIVIQ-14), venous clinical severity score (VCSS), satisfaction (10-point scale), and time to return to normal work. Follow-up rates were 98% at 7 days and 96% at 1 month.ResultsMinor but statistically significant differences were observed in complications such as pain, ecchymosis, edema, and itching between groups. The study group returned to work sooner (2.11 ± 1.19 days) than the control group (4.39 ± 2.55 days, p < 0.01). Patient satisfaction at 1 month and changes in CIVIQ-14 and VCSS scores showed no significant between-group differences.ConclusionElastic stockings worn for 48 h after RFA provided certain advantages over multilayer bandage compression, particularly in reducing early postoperative complications and shortening time to return to work.

PMID:41701921 | DOI:10.1177/02683555261424388

J Am Soc Mass Spectrom. 2026 Feb 17. doi: 10.1021/jasms.5c00372. Online ahead of print.

ABSTRACT

Untargeted tandem mass spectrometry (MS/MS)-based metabolomics enable broad characterization of small molecules in complex samples, yet the majority of spectra in a typical experiment remain unannotated, limiting biological interpretation. Reference data-driven (RDD) metabolomics addresses this gap by contextualizing spectra through comparison to curated, metadata-annotated reference data sets, allowing inference of spectrum origins without requiring exact structural identification. Here, we present an open-source RDD metabolomics platform comprising a user-friendly web application and a Python software package that performs RDD analyses directly from molecular networking outputs generated by GNPS. The tools support visualization and statistical analysis of RDD results, including interactive bar plots, heat maps, principal component analysis, and Sankey diagrams. We illustrate the approach using a hierarchical reference data set of 3500 food items to derive dietary patterns from stool metabolomics data of omnivore and vegan participants. The analysis reveals clear dietary group separation, demonstrating how RDD metabolomics can extract biologically meaningful patterns from otherwise unannotated spectra. Thus, the RDD metabolomics platform removes technical barriers for the metabolomics community to adopting RDD analysis, with the functionality freely available at https://github.com/bittremieuxlab/gnps-rdd and https://gnps-rdd.bittremieuxlab.org/.

PMID:41701920 | DOI:10.1021/jasms.5c00372

Natl Health Stat Report. 2025 Sep 30;(219):1. doi: 10.15620/cdc/174622.

ABSTRACT

OBJECTIVE: This report examines selected measures of healthcare use among teenagers ages 12-17 by parent- or self-report. Agreement between parent-reported and teen self-reported data is also evaluated.

METHODS: The percentage of teenagers with doctor visits, wellness visits, having a usual place of care, having a personal doctor or nurse, and having time alone with a doctor were estimated using teen-reported data from the National Health Interview Survey-Teen collected from July 2021 through December 2023. These estimates were compared with parent-reported estimates from the same time period using data from the National Health Interview Survey. Cohen’s kappa and prevalence-adjusted, bias-adjusted kappa (PABAK) values were used to evaluate agreement between parent and teen responses.

RESULTS: Across all measures, parents reported higher healthcare use for their teenagers than teenagers reported for themselves (for example, 91.4% of parents reported a doctor’s visit in the last 12 months compared with 83.0% of teenagers). Cohen’s kappa values across measures showed fair to slight agreement, with PABAK values showing slightly higher agreement, ranging from slight to substantial. Percentage agreement patterns were most often driven by both parent and teenager affirming healthcare use indicators, except for having time alone with a doctor, which was driven slightly more by the parent and teenager both reporting the teenager had not received this service. Disagreement patterns were driven by parents affirming services the teenager did not; disagreement was highest for having a personal doctor or nurse and time alone with a doctor.

PMID:41701917 | DOI:10.15620/cdc/174622

Natl Health Stat Report. 2025 Dec 16;(220):1. doi: 10.15620/cdc/174626.

ABSTRACT

BACKGROUND: Since 1972, low-density lipoprotein cholesterol (LDL-C) has been calculated by the Friedewald equation, which estimates very low-density lipoprotein cholesterol as triglycerides divided by 5 and is accurate only for triglycerides <400 mg/dL. The Martin equation, published in 2013 (for triglycerides <400 mg/dL), replaced 5 with a factor varying over an array of non-high-density lipoprotein cholesterol and triglyceride levels. This array was extended in 2021 for triglycerides 400-<800 mg/dL. In 2020, the Sampson equation, accurate for triglycerides <800 mg/dL, was developed using multiple least squares regression. This report compares LDL-C as calculated by the Friedewald, Martin, and Sampson equations in a nationally representative sample of adults with triglycerides <400 mg/dL across the distribution of clinical cut points for LDL-C (<70 mg/dL, 70-<100 mg/dL, 100-<160 mg/dL, 160-<190 mg/dL, and ≥190 mg/dL) to assess the impact of equation choice on national estimates.

METHODS: Using data on 4,461 adults in the 2015-2018 National Health and Nutrition Examination Survey, classification agreement into the LDL-C categories used for clinical management across the three equations was assessed using kappa statistics for men and women overall and by demographic subgroups. A sensitivity analysis assessed classification agreement between the Martin and Sampson equations for adults with triglycerides <800 mg/dL.

RESULTS: During 2015-2018, 9.8%-10.0% of adults age 20 and older had LDL-C levels <70 mg/dL (Friedewald: 10.0%, Martin: 9.8%, Sampson: 9.8%). Less than 3% had LDL-C >190 mg/dL (Friedewald: 2.3%, Martin: 2.4%, Sampson: 2.6%). Very good agreement between the equations was seen in all subgroups (kappa >0.8).

CONCLUSIONS: The three equations for LDL-C produce similar U.S. population-level percent distributions for adults age 20 and older across LDL-C categories.

PMID:41701902 | DOI:10.15620/cdc/174626

Can Urol Assoc J. 2026 Feb 13. doi: 10.5489/cuaj.9476. Online ahead of print.

ABSTRACT

INTRODUCTION: Automated segmentation using artificial intelligence (AI) has the potential to rapidly perform three-dimensional (3-D) segmentation of small renal masses (SRM). The objective of this study was to test for clinically and statistically significant differences in time spent segmenting, accuracy, and reliability when comparing manual and automated segmentation of computed tomography (CT) scans with SRM.

METHODS: Patients with a CT scan, SRM <4 cm, and renal neoplasm were identified through an institutional database. Of the 854 patients identified, 184 were excluded. Forty test cases were randomly selected. There were 630 cases for training (using nnU-Net) to which 488 cases from the KiTS23 open-source data set were added. Each of the test cases was segmented by a radiologist, a urologist, and the AI model. Time to segment and Dice coefficients were compared. Deidentified segmented CTs were provided to two independent radiologists who attempted to identify the segmentor and rated the acceptability of the segmented images on a five-point Likert scale.

RESULTS: There were 39 cases with complete timing data. The median time for the AI model to segment was one third of the radiologist’s (152.4 s, interquartile range [IQR] 120.9-177.8 vs. 450 s, IQR 318.8-551.2) and about one-fifth of the urologist’s (800.0 s, IQR 492.0-1538.0) (p<0.001). There was a high degree of inter-rater reliability (median Dice coefficients 0.86-0.90, p=0.09). The scoring radiologists were able to correctly identify the true segmentor in 61.6% of cases (p <0.001). The AI segmentations were scored highest among the three segmentors (median score 4.1/5, standard deviation [SD] 1.0) compared to 3.8 (SD 0.7) for the radiologist, and 3.3 (SD 0.7) for the urologist.

CONCLUSIONS: Automated segmentation of CT scans for patients with SRM was efficient, accurate, and acceptable in this study. This approach has the potential to greatly improve the clinical use of radiomics to assess medical images for these patients.

PMID:41701890 | DOI:10.5489/cuaj.9476

Nurs Educ Perspect. 2026 Mar-Apr 01;47(2):96-100. doi: 10.1097/01.NEP.0000000000001491. Epub 2026 Feb 16.

ABSTRACT

AIM: The study explored the relationship between self-care practices and resilience in baccalaureate nursing faculty before, during, and after the COVID-19 pandemic.

BACKGROUND: There is limited research on self-care and resilience in nurse faculty, yet certain factors have been determined to negatively impact work-life balance.

METHOD: A cross-sectional research survey collected quantitative data on demographics, self-care practices, and resilience. Surveys were sent to nurse faculty within a large statewide system (n = 312).

RESULTS: Before the pandemic, neither demographic characteristics nor self-care was significant predictors of resilience; during and after, however, there was a statistically significant relationship between self-care behaviors and resilience (p = .007, p = .018). Faculty reporting higher levels of self-care measured higher in resilience.

CONCLUSION: Encouraging self-care practices in nurse faculty can enhance resilience and potentially improve overall well-being. Further research on factors of influence would be of benefit.

PMID:41701857 | DOI:10.1097/01.NEP.0000000000001491

Nurs Educ Perspect. 2026 Mar-Apr 01;47(2):86-95. doi: 10.1097/01.NEP.0000000000001492. Epub 2026 Feb 16.

ABSTRACT

AIM: The aim of this study was to examine the relationships between work efforts and rewards with intention to leave and burnout among full-time nurse educators.

BACKGROUND: The nursing faculty shortage is a contributor to the nursing shortage. Poor balance between work efforts and rewards may contribute to intention to leave.

METHOD: A cross-sectional survey of US nurse faculty examined work efforts, rewards, burnout, and intention to leave. Structural equation modeling was used to evaluate the relationships among variables.

RESULTS: Among 588 participants, efforts and rewards both had significant effects on burnout. Burnout and rewards had significant effects on intention to leave. Relationships between efforts, rewards, and intention to leave were significantly mediated by burnout. Efforts, rewards, and burnout all had significant total effects on intention to leave.

CONCLUSION: Interrelationships linking effort, rewards, and burnout require thoughtful solutions focusing on balancing efforts and rewards while addressing dissatisfaction with the nursing faculty role.

PMID:41701856 | DOI:10.1097/01.NEP.0000000000001492

Proc Natl Acad Sci U S A. 2026 Feb 24;123(8):e2522228123. doi: 10.1073/pnas.2522228123. Epub 2026 Feb 17.

ABSTRACT

The presence of high levels of PM_2.5 pollution is a major health threat in India. We evaluate the benefits of reducing ambient PM_2.5 and household air pollution from a 2019 baseline, when over 70% of the population of India was exposed to annual average PM_2.5 of more than 40 μg/m³ and 56% of households burned solid fuels for cooking. We estimate the health benefits from reduced mortality that arise when lowering ambient PM_2.5 to the India standard (40 μg/m³) and to two World Health Organization (WHO) interim standards (25 μg/m³ and 15 μg/m³). The benefits of the ambient standards are moderated by the high levels of household air pollution to which people are exposed due to the concavity of the exposure-response function. We therefore examine the benefits of achieving each standard paired with a 50% reduction in the percentage of households burning solid fuels. Annual mortality reductions from the combined policies exceed 300,000 lives saved for the India standard, 500,000 lives saved for the WHO 25 μg/m³ interim standard, and 675,000 lives saved for the WHO 15 μg/m³ interim standard. The economic benefits of each of these joint policies equal 2.2%, 3.6%, and 4.9% of Indian gross domestic product (GDP), using a Value per Statistical Life of 100 times per capita GDP. Results underscore the importance of considering household air pollution when evaluating ambient standards and the synergistic benefits of combined policies when the exposure-response function is concave.

PMID:41701846 | DOI:10.1073/pnas.2522228123

Proc Natl Acad Sci U S A. 2026 Feb 24;123(8):e2510881123. doi: 10.1073/pnas.2510881123. Epub 2026 Feb 17.

ABSTRACT

Quantum computing improves substantially on known classical algorithms for various important problems, but the nature of the relationship between quantum and classical computing is not yet fully understood. This relationship can be clarified by free models, that add to classical computing just enough physical principles to represent quantum computing and no more. Here, we develop an axiomatization of quantum computing that replaces the standard continuous postulates with a small number of discrete equations, as well as a free model that replaces the standard linear-algebraic model with a category-theoretical one. The axioms and model are based on reversible classical computing, isolate quantum advantage in the ability to take certain well-behaved square roots, and link to various quantum computing hardware platforms. This approach allows combinatorial optimization, including brute force computer search, to optimize quantum computations. The free model may be interpreted as a programming language for quantum computers, that has the same expressivity and computational universality as the standard model, but additionally allows automated verification and reasoning.

PMID:41701823 | DOI:10.1073/pnas.2510881123

Proc Natl Acad Sci U S A. 2026 Mar 3;123(9):e2524764123. doi: 10.1073/pnas.2524764123. Epub 2026 Feb 17.

ABSTRACT

Giant planets and brown dwarfs play a crucial role in star and planet formation as they are situated at the boundary between planets and stars with uncertain formation mechanisms. Previous observational searches for the formation boundary were hampered by the lack of large unified samples of wide-orbit giant planets and substellar companions. A combined analysis of radial velocity and astrometry mitigates this problem and has significantly enlarged the sample. Here, we present a rigorous statistical analysis of the sample of 55 giant planets, brown dwarfs, and low-mass stellar companions orbiting FGK stars. We quantitatively analyze the occurrence rates of brown dwarfs and identify a distinct brown dwarf desert at approximately 30 M_J, with no evidence of disappearance up to 20 au. Unlike previous studies that predicted a declining planet occurrence rate beyond the water-ice line, we identify a population of giant planets and low-mass brown dwarfs in this region. The metallicity and eccentricity trends in our sample suggest that these are the consequences of two different formation scenarios. Our combined population synthesis model successfully accounts for the observed brown dwarf desert, supporting the dual formation hypothesis.

PMID:41701813 | DOI:10.1073/pnas.2524764123