Tag: statistics

PLoS One. 2025 Mar 10;20(3):e0306266. doi: 10.1371/journal.pone.0306266. eCollection 2025.

ABSTRACT

Climate change impacts require us to reexamine crop growth and yield under increasing temperatures and continuing yearly climate variability. Agronomic and agro-meteorological variables were concorded for a large number of plantings of green bean (Phaseolus vulgaris L.) in three growing seasons over several years from semi-tropical Queensland. Using the Queensland government’s SILO meteorological database matched to sowing dates and crop phenology, we derived planting specific agro-meteorological variables. Linear and nonlinear statistical models were used to predict duration of vegetative and pod filling periods and fresh yield using agro-meteorological variables including thermal time, radiation and days of high temperature stress. High temperatures over 27.5∘C and 30∘C in the pod fill period were associated with a lower fresh bean yield. Differences between specific bean growing sites were examined using our bespoke open source software to derive agro-meteorological variables. Agronomically informed statistical models using production data were useful in predicting time of harvest. These methods can be applied to other commercial crops when crop phenology dates are collected.

PMID:40063884 | DOI:10.1371/journal.pone.0306266

Eur J Prev Cardiol. 2025 Mar 10:zwaf135. doi: 10.1093/eurjpc/zwaf135. Online ahead of print.

ABSTRACT

AIMS: Current risk prediction models, such as the Norwegian NORRISK 2, explain only a modest proportion of cardiovascular disease (CVD) incidence. This study aimed to develop improved sex-specific models for predicting the 10-year CVD risk as well as sex- and age-specific thresholds for intervention.

METHODS: Data from 31,946 participants (40-79 years) without prior CVD were analyzed. Data were randomly split into a training set (for estimation) and a test set (for model evaluation). An extreme gradient boosting (XGBoost) model was used to identify the most important predictive variables. Next, prediction models were developed on the training set for each sex separately using XGBoost and logistic regression. The models were evaluated on the test set using receiver-operating characteristic (ROC) and precision recall (PR) curves. Finally, age- and sex-specific thresholds for intervention were explored.

RESULTS: All traditional risk factors included in NORRISK 2 and the European SCORE2 model were important predictors for males, but not for females. Potential new risk predictors were identified. The XGBoost model improved CVD risk prediction for males: 0.013- and 0.012-unit increase in ROC-AUC compared to NORRISK 2 and SCORE2 respectively, and 12% and 11% increase in PR-AUC respectively. For females, neither the XGBoost nor logistic regression model performed significantly better than NORRISK 2 and SCORE2. Age- and sex-specific thresholds showed an improvement in sensitivity compared with NORRISK 2-suggested thresholds.

CONCLUSIONS: By employing statistical learning and incorporating sex-specific risk factors, we propose improved risk prediction models for CVD in males. Introducing sex-specific thresholds for intervention could enhance CVD prevention for both sexes.

PMID:40063873 | DOI:10.1093/eurjpc/zwaf135

PLoS Negl Trop Dis. 2025 Mar 10;19(3):e0012918. doi: 10.1371/journal.pntd.0012918. Online ahead of print.

ABSTRACT

BACKGROUND: COVID-19 pandemic had unprecedented global impact on health and society, highlighting the need for a detailed understanding of SARS-CoV-2 evolution in response to host and environmental factors. This study investigates the evolution of SARS-CoV-2 via mutation dynamics, focusing on distinct age cohorts, geographical location, and vaccination status within the Indian population, one of the nations most affected by COVID-19.

METHODOLOGY: Comprehensive dataset, across diverse time points during the Alpha, Delta, and Omicron variant waves, captured essential phases of the pandemic’s footprint in India. By leveraging genomic data from Global Initiative on Sharing Avian Influenza Data (GISAID), we examined the substitution mutation landscape of SARS-CoV-2 in three demographic segments: children (1-17 years), working-age adults (18-64 years), and elderly individuals (65+ years). A balanced dataset of 69,975 samples was used for the study, comprising 23,325 samples from each group. This design ensured high statistical power, as confirmed by power analysis. We employed bioinformatics and statistical analyses, to explore genetic diversity patterns and substitution frequencies across the age groups.

PRINCIPAL FINDINGS: The working-age group exhibited a notably high frequency of unique substitutions, suggesting that immune pressures within highly interactive populations may accelerate viral adaptation. Geographic analysis emphasizes notable regional variation in substitution rates, potentially driven by population density and local transmission dynamics, while regions with more homogeneous strain circulation show relatively lower substitution rates. The analysis also revealed a significant surge in unique substitutions across all age groups during the vaccination period, with substitution rates remaining elevated even after widespread vaccination, compared to pre-vaccination levels. This trend supports the virus’s adaptive response to heightened immune pressures from vaccination, as observed through the increased prevalence of substitutions in important regions of SARS-CoV-2 genome like ORF1ab and Spike, potentially contributing to immune escape and transmissibility.

CONCLUSION: Our findings affirm the importance of continuous surveillance on viral evolution, particularly in countries with high transmission rates. This research provides insights for anticipating future viral outbreaks and refining pandemic preparedness strategies, thus enhancing our capacity for proactive global health responses.

PMID:40063870 | DOI:10.1371/journal.pntd.0012918

Eur Radiol. 2025 Mar 10. doi: 10.1007/s00330-025-11449-9. Online ahead of print.

ABSTRACT

BACKGROUND: Recognising bone injuries in children is a critical part of children’s imaging, and, recently, several AI algorithms have been developed for this purpose, both in research and commercial settings. We present an updated systematic review of the literature, including the latest developments.

METHODS/MATERIALS: Scopus, Web of Science, Pubmed, Embase, and Cochrane Library databases were queried for studies published between 1 January 2011 and 6 September 2024 matching search terms ‘child’, ‘AI’, ‘fracture,’ and ‘imaging’. Retrieved studies were evaluated, and descriptive statistics were collated for diagnostic performance.

RESULTS: Twenty-six eligible articles were included; seventeen (17/26, 65.%) of these were published within the last two years. Six studies (6/26, 23.1%) used open-source datasets to train their algorithm, the remainder used local data. Sixteen studies (16/26, 61.5%) evaluated a single joint (wrist, elbow, or ankle); multiple bones within the appendicular skeleton were assessed in the other ten studies. Seven articles (7/26, 26.9%) related to the performance of a commercial AI tool. Accuracy of AI models ranged from 85.0 to 100.0%. Six studies (6/26, 23.1%) evaluated the accuracy of human readers with and without AI assistance, of which two studies found a statistically significant improvement when humans were assisted by AI. The largest pool of human readers in any paper consisted of 11 readers of varying experience.

CONCLUSION: The pace of research in AI fracture detection in children’s imaging has increased. Studies show high accuracy of AI models, but proof of clinical impact, cost-effectiveness, and any socioeconomic or ethical bias are still lacking.

KEY POINTS: Question AI model development has rapidly increased in recent years. We present the latest developments in AI model diagnostic accuracy for paediatric fracture detection. Findings Studies now demonstrate performance improvement when AI is used to assist human interpretation of paediatric fractures, especially when aiding junior radiologists. Clinical relevance Studies show high accuracy for AI models; however, further research is needed to evaluate AI across diverse age groups, bone diseases, and fracture types. Evidence of real-world patient benefit for AI and any socioeconomic or ethical bias are still lacking.

PMID:40063108 | DOI:10.1007/s00330-025-11449-9

J Orofac Orthop. 2025 Mar 10. doi: 10.1007/s00056-025-00576-z. Online ahead of print.

ABSTRACT

OBJECTIVES: The primary objective of this study was to investigate the feasibility of magnetic resonance imaging (MRI) usage over computed tomography (CT) to perform three-dimensional (3D) cephalometric analyses. The secondary objective is to find intra- and interobserver reliability of manual cephalometric landmarks identification in both CT and MRI scan data.

METHODS: Data from 40 patients were used in this study, with orthodontists manually identifying 37 landmarks on both CT and MRI scans. The interclass correlation coefficient (ICC) was calculated individually for both CT and MRI scan data to find intra- and interobserver reliability. In addition to ICC, paired t‑test and mean error were also calculated. Ground truth landmarks were calculated by considering the mean values of manually located 37 landmarks by observers for both CT and MRI. Thirty-seven cephalometric measurements (29 linear, 6 angular, and 2 ratios) were measured using 37 ground truth landmarks. Mean error (ME) between CT and MRI measurements was calculated and paired t‑test was performed to find the reliability of MRI usage over CT. Bland-Altman analysis was also performed on the measurements to check the agreement between CT and MRI.

RESULTS: The intra- and interobserver reliability was found to be reliable (ICC > 0.98, and P > 0.05) for all 37 landmarks in both CT and MRI. The ME for linear measurements was found to be 1.81 mm for hard tissue, 1.72 mm for soft tissue, and 1.53° for hard tissue angular measurements between CT and MRI. The paired t‑test performed on measurements between CT and MRI proved to be statistically insignificant (p > 0.05). The Bland-Altman analysis also showed strong agreement and low systemic bias between CT and MRI data.

CONCLUSIONS: The strong ICC and P values shows the high reliability and reproducibility of manual landmark identification on both CT and MRI. The ME for the linear and angular measurements between CT and MRI was found to be well within acceptable limits. The results of paired t‑test and Bland-Altman analyses for cephalometric measurements between CT and MRI has shown strong evidence supporting the use of MRI as a substitute for CT.

PMID:40063106 | DOI:10.1007/s00056-025-00576-z

Int J Biometeorol. 2025 Mar 10. doi: 10.1007/s00484-025-02886-8. Online ahead of print.

ABSTRACT

The association of low-level ozone (O₃) exposure with the mortality risk of ischemic heart disease (IHD) and stroke remains to be investigated. This study aimed to investigate the relationship between low-level O₃ exposure and mortality risk of IHD and stroke in Yibin, a city in southwestern China. A Poisson distribution lagged nonlinear model was used to assess the effect of O₃ exposure on IHD and stroke mortality and to explore the susceptible population according to gender and age subgroups and the susceptible season according to seasonal subgroups and to analyse the health effects under low O₃ exposure compared with high O₃ exposure. The mean O₃ exposure concentration from 2014 to 2020 was approximately 48.3 μg/m3. There was a major lagged effect of O₃ exposure on IHD and stroke. For every 10.0 μg/m³ increase in O₃ concentration, the cumulative risks of death for the two diseases were 1.0211 (95% Confidence Interval [CI]: 1.0064, 1.0358) and 1.0211 (95% CI: 1.0064, 1.0357), respectively. The mortality risks of IHD and stroke for women were 1.0064 (95% CI: 1.0016, 1.0113) and 1.0030 (95% CI: 1.0008, 1.0051), and for those aged > 65 years, they were 1.0082 (95% CI: 1.0026, 1.0139) and 1.0018 (95% CI: 1.0002, 1.0034), and the mortality risks in the warm season were 1.0043 (95% CI: 1.0007, 1.0080) and 1.0038 (95% CI: 1.0005, 1.0072), respectively. The introduction of other pollutants (PM_2.5, PM₁₀, NO₂, CO) to construct a dual-pollutant model showed that the effect of O₃ on the mortality risk of IHD and stroke remained statistically significant. This study consolidates the evidence for a positive correlation between low-level O₃ exposure and the mortality risk of IHD and stroke. The findings provide preliminary exploratory insights into the potential impact of air pollution on these diseases, offering a valuable reference for future research.

PMID:40063105 | DOI:10.1007/s00484-025-02886-8

J Acoust Soc Am. 2025 Mar 1;157(3):1650-1664. doi: 10.1121/10.0036053.

ABSTRACT

In a recent research paper, we introduced the statistical wave field theory, which establishes the statistical laws of waves propagating in a bounded volume. These laws hold after many reflections on the boundary surface and at high frequency. The statistical wave field theory is the first statistical theory of reverberation that provides the closed-form expression of the power distribution and the correlations of the wave field jointly over time, frequency and space, in terms of the geometry and the specific admittance of the boundary surface. In this paper, we refine the theory predictions, by investigating the impact of a curved boundary surface on the wave field statistics. In particular, we provide an improved closed-form expression of the reverberation time in room acoustics that holds at lower frequency.

PMID:40063083 | DOI:10.1121/10.0036053

J Am Med Inform Assoc. 2025 Mar 10:ocaf023. doi: 10.1093/jamia/ocaf023. Online ahead of print.

ABSTRACT

OBJECTIVES: Large language models (LLMs) are increasingly utilized in healthcare, transforming medical practice through advanced language processing capabilities. However, the evaluation of LLMs predominantly relies on human qualitative assessment, which is time-consuming, resource-intensive, and may be subject to variability and bias. There is a pressing need for quantitative metrics to enable scalable, objective, and efficient evaluation.

MATERIALS AND METHODS: We propose a unified evaluation framework that bridges qualitative and quantitative methods to assess LLM performance in healthcare settings. This framework maps evaluation aspects-such as linguistic quality, efficiency, content integrity, trustworthiness, and usefulness-to both qualitative assessments and quantitative metrics. We apply our approach to empirically evaluate the Epic In-Basket feature, which uses LLM to generate patient message replies.

RESULTS: The empirical evaluation demonstrates that while Artificial Intelligence (AI)-generated replies exhibit high fluency, clarity, and minimal toxicity, they face challenges with coherence and completeness. Clinicians’ manual decision to use AI-generated drafts correlates strongly with quantitative metrics, suggesting that quantitative metrics have the potential to reduce human effort in the evaluation process and make it more scalable.

DISCUSSION: Our study highlights the potential of a unified evaluation framework that integrates qualitative and quantitative methods, enabling scalable and systematic assessments of LLMs in healthcare. Automated metrics streamline evaluation and monitoring processes, but their effective use depends on alignment with human judgment, particularly for aspects requiring contextual interpretation. As LLM applications expand, refining evaluation strategies and fostering interdisciplinary collaboration will be critical to maintaining high standards of accuracy, ethics, and regulatory compliance.

CONCLUSION: Our unified evaluation framework bridges the gap between qualitative human assessments and automated quantitative metrics, enhancing the reliability and scalability of LLM evaluations in healthcare. While automated quantitative evaluations are not ready to fully replace qualitative human evaluations, they can be used to enhance the process and, with relevant benchmarks derived from the unified framework proposed here, they can be applied to LLM monitoring and evaluation of updated versions of the original technology evaluated using qualitative human standards.

PMID:40063081 | DOI:10.1093/jamia/ocaf023

J Med Internet Res. 2025 Mar 10;27:e53823. doi: 10.2196/53823.

ABSTRACT

BACKGROUND: Smartphone mobile health (mHealth) apps have the potential to enhance access to health care services and address health care disparities, especially in low-resource settings. However, when developed without attention to equity and inclusivity, mHealth apps can also exacerbate health disparities. Understanding and creating solutions for the disparities caused by mHealth apps is crucial for achieving health equity. There is a noticeable gap in research that comprehensively assesses the entire spectrum of existing health apps and extensively explores apps for specific health priorities from a health care and public health perspective. In this context, with its vast and diverse population, India presents a unique context for studying the landscape of mHealth apps.

OBJECTIVE: This study aimed to create a comprehensive dataset of mHealth apps available in India with an initial focus on heart disease (HD)-related apps.

METHODS: We collected individual app data from apps in the “medical” and “health and fitness” categories from the Google Play Store and the Apple App Store in December 2022 and July 2023, respectively. Using natural language processing techniques, we selected HD apps, performed statistical analysis, and applied latent Dirichlet allocation for clustering and topic modeling to categorize the resulting HD apps.

RESULTS: We collected 118,555 health apps from the Apple App Store and 108,945 health apps from the Google Play Store. Within these datasets, we found that approximately 1.7% (1990/118,555) of apps on the Apple App Store and 0.5% (548/108,945) on the Google Play Store included support for Indian languages. Using monograms and bigrams related to HD, we identified 1681 HD apps from the Apple App Store and 588 HD apps from the Google Play Store. HD apps make up only a small fraction of the total number of health apps available in India. About 90% (1496/1681 on Apple App Store and 548/588 on Google Play Store) of the HD apps were free of cost. However, more than 70% (1329/1681, 79.1% on Apple App Store and 423/588, 71.9% on Google Play Store) of HD apps had no reviews and rating-scores, indicating low overall use.

CONCLUSIONS: Our study proposed a robust method for collecting and analyzing metadata from a wide array of mHealth apps available in India through the Apple App Store and Google Play Store. We revealed the limited representation of India’s linguistic diversity within the health and medical app landscape, evident from the negligible presence of Indian-language apps. We observed a scarcity of mHealth apps dedicated to HD, along with a lower level of user engagement, as indicated by reviews and app ratings. While most HD apps are financially accessible, uptake remains a challenge. Further research should focus on app quality assessment and factors influencing user adoption.

PMID:40063078 | DOI:10.2196/53823

Jpn J Clin Oncol. 2025 Mar 10:hyaf047. doi: 10.1093/jjco/hyaf047. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aimed to assess the prognostic outcomes and risk of adverse events in elderly non-muscle invasive bladder cancer (NMIBC) patients receiving photodynamic diagnosis-assisted transurethral resection of bladder cancer (PDD-TURBT).

METHODS: This study retrospectively included 326 patients who were over 70 years old and received either PDD-TURBT (n = 114, PDD group) or white-light TURBT (n = 212, WL group). Oncological outcomes, namely recurrence-free survival (RFS) and progression-free survival (PFS), and adverse event profiles were compared between the two groups.

RESULTS: In the PDD and WL groups, the median RFS periods were not reached and 41.7 months (P < 0.001), and the median PFS periods were not reached and 160.2 months (P = 0.057), respectively. The Grey test which take account to overall death as a competing risk event revealed recurrence tended to decrease in PDD group (P = 0.050). The independent prognostic factors were determined by multivariate Cox regression analyses: WL-TURBT in RFS. After propensity score matching, statistically favorable RFS in the PDD group were shown (P = 0.018). The incidence of AST/ALT elevation and intraoperative hypotension (defined as systolic blood pressure ≤ 80 mmHg) were significantly higher in the PDD group than in the WL group (P = 0.003 and 0.003, respectively).

CONCLUSIONS: Prolonged RFS are expected for PDD-TURBT using oral 5-aminolevulinic acid in elderly NMIBC patients. However, the risks of liver injury and intraoperative hypotension are higher for PDD-TURBT.

PMID:40063065 | DOI:10.1093/jjco/hyaf047