bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effectiveness of Mobile Health Applications for Cardiometabolic Risk Reduction in Urban and Rural India: A Pilot, Randomized Controlled Study
Authors: Ranjani Harish , Avari Parizad , Nitika Sharma , Jagannathan Narayanaswamy , Oliver Nick , Valabhji Jonathan , Mohan Viswanathan , Chambers John Campbell , Anjana Ranjit Mohan 
Published: January 2025

Introduction: mHealth technology has the potential to deliver personalized health care; however, data on cardiometabolic risk factors are limited. This study aims to assess the effectiveness of mobile health applications (apps) on cardiometabolic risk factor reduction in adults aged 25 to 60 years in urban and rural India. Methods: The study design was a pilot randomized controlled trial conducted in Tamil Nadu, India. Smartphone users (25-60 years) with basic literacy and at high risk of developing diabetes (Indian Diabetes Risk Score ≥30 and/or fasting blood sugar [FBS] 100-125 mg/dL) were recruited. Four mobile apps (two commercially available, two novel) for cardiometabolic risk reduction were evaluated. Primary outcome (weight loss) was analyzed using intention-to-treat analysis with post hoc analysis and logistic regression models adjusted for confounders. Results: A total of 5264 participants were screened, and 610 were recruited into the study. Participants (7%) dropped out largely due to the COVID-19 pandemic. Data from 567 participants were used for the final analysis. In the intention-to-treat analysis, a significant reduction in body weight was observed in the intervention group as compared with control, more so in the urban (−2.40 kg, 95% confidence interval [CI] = [−3.10, −1.69], P < .001) compared with rural population (−1.19 kg, 95% CI = [−1.55, −0.82], P < .001). Intervention group participants showed significant reductions in body mass index, waist circumference, blood pressure, FBS, total serum cholesterol, and a positive effect on dietary and physical activity behaviors compared with controls. Conclusions: mHealth interventions can reduce diabetes risk, improve cardiometabolic health, and improve lifestyle behaviors in South Asian populations. Trial Registration: The trial is registered with the Central Trials Registry, India (CTRI/2020/03/024327).
Translation, Validity, and Reliability of the Indonesian Version of the Shoulder Pain and Disability Index (SPADI)
Authors: Deviandri Romy , Daud Afrianto , Utami Tania Nugrah , Octarina Putri , Aminata Iman W. , Alkaff Firas Farisi 
Published: January 2025

Background: The Shoulder Pain and Disability Index (SPADI) is a widely used 13-item shoulder-specific patient-reported outcome measure for shoulder pain disorders. The English version of SPADI is easy to use and demonstrates excellent measurement properties for clinical and research settings. Purpose: To translate and culturally adapt an Indonesian version of SPADI (SPADI-IDN) and then validate its use in Indonesian patients. Study Design: Cohort study (Diagnosis); Level of evidence, 2. Methods: Through a forward and a backward translation process, the validity of the questionnaire was investigated. The study population was 100 patients with shoulder pain who were treated in a hospital in Indonesia. The patients were asked to fill out the SPADI-IDN during their visit. To evaluate the validity of SPADI-IDN, the patients were also asked to fill out the Medical Outcomes Study 12-Item Short-Form Health Survey and the Oxford Shoulder Score. To assess the test-retest reliability, the same patients were asked to fill out the SPADI-IDN questionnaire again 1 week later. The assessment of construct validity, test-retest reliability, internal consistency, floor and ceiling effect, and measurement of error followed the COSMIN (Consensus-based Standards for the selection of health Measurement Instruments) guidelines. The Bland-Altman method was used to explore absolute agreement. Results: Of the 100 eligible patients, all were used to assess construct validity, and 87 patients (87%) were used to assess test-retest reliability. Almost every established hypothesis about the correlations between SPADI-IDN and other questionnaires could be confirmed, implying good construct validity. No floor or ceiling effects were found. The intraclass correlation value was 0.99, indicating strong test-retest reliability. A Cronbach α was 0.95, indicating a good internal consistency. The Bland-Altman analysis did not reveal any bias. The standard error of measurement and the minimal detectable change at the individual and group levels were 2.65, 7.3, and 0.7, respectively. Conclusion: The SPADI-IDN represents a valid and reliable tool for measuring pain and disability in patients with shoulder pain disorders.
Particle size distribution of Japanese cedar chip converted from boards using a disc chipper and its relationships with disc rotation speed and board feed speed
Authors: Matsuda Yosuke , Matsumura Yukari , Yanagida Takashi , Fujimoto Kiyohiko 
Published: January 2025

Abstract
The effects of disc chipper rotation speed and raw material feed speed on the particle size distribution of wood chips produced were investigated to develop an efficient technique for producing ideal sized chips for the stable operation of combined heat and power plants. The particle size distribution of wood chips was measured using a screening method and approximated to a Rosin–Rammler distribution to calculate the median of the chip. The results suggest that chip size decreased as the rotation speed increased from 560 to 720 rpm, but no clear effect of feed speed on the chip size was observed. The Rosin–Rammler equation was found to be appropriate to explain the size distribution of wood chips. Two types of screening sieves, woven wire mesh sieve and perforated plate sieve, were used for the screening. It was found that the woven wire mesh sieve may underestimate the size of the chip, although the Rosin–Rammler equation derived from the woven wire mesh sieve was able to be converted to the equation for the perforated plate sieve.
Predicting the risk of hematoma expansion in acute intracerebral hemorrhage: the GIVE score
Authors: Yang Tian-Nan , Lv Xin-Ni , Wang Zi-Jie , Hu Xiao , Zhao Li-Bo , Cheng Jing , Li Qi 
Published: January 2025

Abstract

Background
Numerous noncontrast computed tomography (NCCT) markers have been reported and validated as effective predictors of hematoma expansion (HE). Our objective was to develop and validate a score based on NCCT markers and clinical characteristics to predict risk of HE in acute intracerebral hemorrhage (ICH) patients.

Methods
We prospectively collected spontaneous ICH patients at the First Affiliated Hospital of Chongqing Medical University to form the development cohort (n = 395) and at the Third Affiliated Hospital of Chongqing Medical University to establish the validation cohort (n = 139). We adopted a revised HE definition, incorporating the standard definition of HE (> 6 mL or > 33%) and intraventricular hemorrhage (IVH) expansion (any new IVH or IVH expansion ≥ 1 ml). The predictive score was formulated based on the parameter estimates derived from the multivariable logistic regression analysis.

Result
The Glasgow Coma Scale, island sign, ventricular hemorrhage and time elapsed from onset to NCCT scan (GIVE) score was created as a total of individual points (0–6) based on Glasgow Coma Scale (2 points for ≤ 11), island sign (1 point for presence), ventricular hemorrhage (1 point for presence), and time elapsed from onset to NCCT scan (2 points for ≤ 2.5 h). The c statistic was 0.72(95% confidence interval [CI], 0.66–0.78) and 0.73(95% CI, 0.63–0.82) in the development and validation cohorts, respectively.

Conclusion
A six-point scoring algorithm has been developed and validated to assess the risk of HE in patients with ICH. This scoring system facilitates the rapid and accurate identification of patients at increased risk for HE.
Outcome of Er, Cr:YSGG laser and antioxidant pretreatments on bonding quality to caries-induced dentin
Authors: Moharam Lamiaa M. , Salem Haidy N. , Abdou Ahmed , Afifi Rasha H. 
Published: January 2025

Abstract

Background
This study aimed to assess the influence of different pretreatment protocols and antioxidants application on the shear bond strength (SBS) of universal adhesive to sound (SoD) and caries-induced dentin (CID).

Methods
One hundred and twenty posterior teeth had their occlusal enamel removed, then the specimens were divided into two main groups according to dentin substrates; SoD and CID, three subgroups according to pretreatments protocols control (no pretreatment), NaOCl-treated, and Er, Cr:YSGG-treated and two divisions according to antioxidant application (with and without sodium ascorbate (SA) application). All-Bond Universal (ABU) universal adhesives was applied in self-etch (SE) mode then resin composite discs were built. The specimens were stored in distilled water for 24-hr at 37°C before SBS testing. Three-way ANOVA and Tukey HSD tests were used for data analysis (a = 0.05).

Results
6% NaOCl resulted in a significant reduction in SBS in SoD without antioxidant application. 10% SA application showed significant increase in SBS for 6% NaOCl group only in SoD. Laser application recorded a significantly higher SBS compared to 6% NaOCl group without or with antioxidant application, while 10% SA application revealed a significant increase in SBS for control group only.

Conclusions
Er, Cr:YSGG laser irradiation followed by antioxidant application has the potential to enhance the bonding quality of both tested dentin substrates. NaOCl application has significantly compromised the bonding to SoD and CID substrates.
Combining theory and usability testing to inform optimization and implementation of an online primary care depression management tool
Authors: McCleary Nicola , Presseau Justin , Perkins Isabelle , Mutsaers Brittany , Kendall Claire E. , Yamada Janet , Gillis Katharine , Green Douglas 
Published: January 2025

Abstract

Background
The ‘Ottawa Depression Algorithm’ is an evidence-based online tool developed to support primary care professionals care for adults with depression. Uptake of such tools require provider behaviour change. Identifying issues which may impact use of an innovation in routine practice (i.e. barriers to and enablers of behaviour change) informs the selection of implementation strategies that can be deployed with the tool to support use. However, established theory-informed barriers/enablers assessment methods may be less well suited to identifying issues with tool usability. User testing methods can help to determine whether the tool itself is optimally designed. We aimed to integrate these two methodological approaches to i) identify issues impacting the usability of algorithm; and ii) identify barriers to and enablers of algorithm use in routine practice.

Methods
We conducted semi-structured interviews with primary care professionals in Ottawa, Canada. To evaluate usability, participants used a written patient scenario to work through the algorithm while verbalizing their thoughts (‘Think Aloud’). Participants were then asked about factors influencing algorithm use in routine practice (informed by the Theoretical Domains Framework). We used the codebook approach to thematic analysis to assign statements to pre-specified codes and develop themes pertaining to usability and routine use.

Results
We interviewed 20 professionals from seven practices. Usability issues were summarised within five themes: Optimizing content and flow to align with issues faced in practice, Enhancing the most useful algorithm components, Interactivity of the algorithm and embedded tools, Clarity of presence, purpose, or function of components, and Navigational challenges and functionality of links. Barriers to and enablers of routine use were summarised within five themes: Getting to know the algorithm, Alignment with roles and pathways of influence, Integration with current ways of working, Contexts for use, and Anticipated benefits and concerns about patient communication.

Conclusions
Whilst the Ottawa Depression Algorithm was viewed as a useful tool, specific usability issues and barriers to use were identified. Supplementing a theory-based barriers/enablers assessment with usability testing provided enhanced insights to inform optimization and implementation of this clinical tool. We have provided a methods guide for others who may wish to apply this approach.
Evaluating catastrophic health expenditures among patients with long bone fractures in Ghana's major teaching hospitals: a hospital-based analysis
Authors: Buunaaim Alexis D. B. , Amenah Michel Adurayi , Konadu-Yeboah Dominic , Baidoo Richard , Asare Amgbo , Addo Wilfred Larbi , Martin Claude 
Published: January 2025

Abstract

Background
In low and middle-income countries like Ghana, out-of-pocket (OOP) payments remain a significant barrier to healthcare access, often leading to catastrophic health expenditures (CHE). This study evaluates the incidence of CHE among patients treated for long bone fractures at Ghana’s major teaching hospitals, providing insight into the economic burdens faced by these patients.

Methods
This cross-sectional study analyzed data from 2,980 patients with long bone fractures treated at four major teaching hospitals in Ghana from July 2017 to July 2020. We collected demographic, clinical, and economic data, including OOP payments and patient-reported income, to assess the incidence of CHE at varying income thresholds (10%, 20%, 30%, 40%). Logistic regression models were used to identify predictors of CHE, with variables including age, gender, education, region, fracture type, injury severity, and NHIS coverage.

Results
The incidence of CHE was highest at the 10% income threshold (53.21%) and decreased with higher thresholds. Male patients incurred higher average OOP payments ($343.68) than females ($271.63), and patients with tibia fractures faced the highest financial burden. Educational and regional disparities were evident, with lower CHE rates among patients with higher educational attainment and those from northern regions. NHIS coverage provided limited financial relief, particularly at lower income thresholds.

Conclusion
Long bone fractures impose a substantial financial burden on patients in Ghana, with significant gender, educational, and regional disparities in OOP payments and CHE. While NHIS provides some relief, it remains inadequate in protecting patients from financial distress. Policy interventions aimed at expanding NHIS coverage, reducing OOP payments for high-cost treatments, and addressing geographic inequities are urgently needed to improve financial protection for patients with long bone fractures in Ghana. Future research should focus on capturing long-term financial impacts and improving income data accuracy to better inform healthcare policies.
Developing an automatic treatment record review system for quality assurance of patient treatment delivery in radiation therapy
Authors: Huang Peng , Xu Yingjie , Huan Fukui , Zhang Yanxin , Ma Min , Men Kuo , Dai Jianrong 
Published: January 2025

Abstract

Background and purpose
Treatment record contains most of information related to treatment plan delivery in radiation therapy. Reviewing treatment record is an important quality assurance (QA) task for safety and quality of patient treatments. This task is usually performed by senior medical physicists. However, it is time-consuming, tedious, and error-prone. To assist this process, a treatment record review system (TRRS) is developed to automatically review items related to treatment delivery record.

Methods
The treatment record is firstly extracted from oncology information system (OIS). Based on the daily patient treatment information, the original plan from the treatment planning system is identified. Then the original plan and the delivered plan are correlated. Eight review categories (parameter consistency, treatment completeness, treatment progression, image guidance, override, treatment couch, documentation, and treatment mode) are created. Tailored rules are designed for various review items to automate the review process. As a result, for each daily treatment record, a reviewed flag (pass, failure, warning, and N/A) is assigned by the TRRS. Finally, this system is evaluated by 6 months patient treatment records collected in our institute and compared to the manual process on the same data.

Results
TRRS processed a total of 76,651 treatment fractions from 4230 patients with an average of 574 treatments per day. The percentage of the detected anomalies among the total records was 0.76%. The average processing time was 3.9 s and 282 s per treatment record for the automatic and manual processes, respectively. Comparing with the manual process, the time efficiency of TRRS is improved by a factor of 72. The average numbers of anomalies detected by the automatic and manual processes are 21 and 13 per day, respectively. TRRS detects 61.5% more anomalies than those of the manual process.

Conclusion
TRRS is not only efficient in processing a large amount of treatment records on a daily basis but also effective in finding more anomalies than those of physics weekly check. The application of the TRRS could significantly reduce the workload of the review physicists and let them focus on more important works related to patient safety.
Evaluating the feasibility of AI-predicted bpMRI image features for predicting prostate cancer aggressiveness: a multi-center study
Authors: Wang Kexin , Luo Ning , Sun Zhaonan , Zhao Xiangpeng , She Lilan , Xing Zhangli , Chen Yuntian , He Chunlei , Wu Pengsheng , Wang Xiangpeng , Kong ZiXuan 
Published: January 2025

Abstract

Objective
To evaluate the feasibility of utilizing artificial intelligence (AI)-predicted biparametric MRI (bpMRI) image features for predicting the aggressiveness of prostate cancer (PCa).

Materials and methods
A total of 878 PCa patients from 4 hospitals were retrospectively collected, all of whom had pathological results after radical prostatectomy (RP). A pre-trained AI algorithm was used to select suspected PCa lesions and extract lesion features for model development. The study evaluated five prediction methods, including (1) A clinical-imaging model of clinical features and image features of suspected PCa lesions selected by AI algorithm, (2) the PIRADS category, (3) a conventional radiomics model, (4) a deep-learning bases radiomics model, and (5) biopsy pathology.

Results
In the externally validated dataset, the deep learning-based radiomics model showed the highest area under the curve (AUC 0.700 to 0.791). It exceeded the clinical-imaging model (AUC 0.597 to 0.718), conventional radiomic model (AUC 0.566 to 0.632), PIRADS score (AUC 0.554 to 0.613), and biopsy pathology (AUC 0.537 to 0.578). The AUC predicted by the model did not show a statistically significant difference among the three externally verified hospitals (p > 0.05).

Conclusion
Deep-learning radiomics models utilizing AI-extracted image features from bpMRI images can potentially be used to predict PCa aggressiveness, demonstrating a generalized ability for external validation.

Critical relevance statement
Predicting the aggressiveness of prostate cancer (PCa) is important for formulating the best treatment plan for patients. The radiomic model based on deep learning is expected to provide an objective and non-invasive method for evaluating the aggressiveness of PCa.

Key Points

Predicting the aggressiveness of PCa is important for patients to obtain the best treatment options.

The deep learning-based radiomics model can predict the aggressiveness of PCa with high accuracy.

The model has good universality when tested on multiple external datasets.

Graphical Abstract
Tacit engagement in humanitarian action: making sense of silence and secrecy in humanitarian negotiations
Authors: Akal Ayse Bala , Roepstorff Kristina , Lidén Kristoffer 
Published: January 2025

Abstract
Secrecy and silence regarding the compromises and trade-offs made by frontline humanitarians in order to achieve access, protection, efficiency and legitimacy are a widely observable but underconceptualized phenomenon in humanitarian action. As a form of “tacit engagement”, it allows humanitarian practitioners to operate in difficult settings but also implies lacking accountability, coordination and learning. There is thus a need for disentangling the productive and restraining effects of secrecy in this field. In this paper, we do so by conceptualizing these practices as forms of “tacit engagement” and relating them to political theory on secrecy and silence. Drawing on insights from expert consultations and qualitative interviews on humanitarian negotiations, we relate it to existing literatures on remote management, risk management and a culture of silence in humanitarian organizations more generally and humanitarian negotiations more specifically. In the conclusion, we work out the potentially productive and destructive effects of tacit engagement as an invitation to ethical assessment.
Deep learning of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study
Authors: Ren Huanhuan , Song Haojie , Cui Shaoguo , Xiong Hua , Long Bangyuan , Li Yongmei 
Published: January 2025

Abstract

Background
Hemorrhagic transformation (HT) is a complication of reperfusion therapy following acute ischemic stroke (AIS). We aimed to develop and validate a model for predicting HT and its subtypes with poor prognosis—parenchymal hemorrhage (PH), including PH-1 (hematoma within infarcted tissue, occupying < 30%) and PH-2 (hematoma occupying ≥ 30% of the infarcted tissue)—in AIS patients following intravenous thrombolysis (IVT) based on noncontrast computed tomography (NCCT) and clinical data.

Methods
In this six-center retrospective study, clinical and imaging data from 445 consecutive IVT-treated AIS patients were collected (01/2018–06/2023). The training cohort comprised 344 patients from five centers, and the test cohort included 101 patients from the sixth center. A clinical model was developed using eXtreme Gradient Boosting, an NCCT-based imaging model was created using deep learning, and an ensemble model integrated both models. Comparison with existing clinical scores (MSS, SEDAN, GRASPS) was performed using the DeLong test.

Results
Of the 445 individuals, 202 (45.4%) had HT, 79 (17.8%) had hemorrhagic infarction, and 123 (27.6%) had PH. In the test cohort, the area under the receiver operating characteristic curve (AUROC) of the clinical, imaging, and ensemble model for HT prediction was 0.877, 0.920, and 0.937, respectively. The ensemble model for HT prediction outperformed MSS, SEDAN, and GRASPS scores (p ≤ 0.023). The ensemble model predicted PH and PH-2 with AUROC of 0.858 and 0.806, respectively.

Conclusion
Developing and validating an integrated model that can predict HT and its subtypes in AIS patients following IVT based on NCCT and clinical data is feasible.

Relevance statement
The clinical, imaging, and ensemble models based on noncontrast CT and clinical data outperformed existing clinical scores in predicting hemorrhagic transformation of AIS and its subtypes with poor prognosis, facilitating personalized treatment decisions.

Key Points

The models demonstrated the capability to predict hemorrhagic transformation of acute ischemic stroke quickly, accurately, and reliably.

The proposed models outperformed existing clinical scores in predicting hemorrhagic transformation.

The ensemble model provided risk assessment of parenchymal hemorrhage and parenchymal hemorrhage-2 outperforming existing clinical scores.

Graphical Abstract