Registration Information Research Articles

Towards more effective health monitoring: a comparison of different data sources

Abstract Background Monitoring the prevalences of chronic diseases and their risk factors at the population level is important to support effective prevention measures, health service planning and health policy-making. The utilization of register information is cost-effective in population health monitoring, but the validity of information is important to assess. The aim of this study was to compare the information on most common chronic diseases and risk factors collected from a population-based Healthy Finland survey and national health registers. Methods The study population comprised of 4785 individuals aged 20 years or over from the nationally representative Healthy Finland Health Examination Survey conducted in 2023. Information on the most common chronic diseases were derived from both survey data (including measured and self-reported data) and the national registers. The prevalence in survey data were compared with prevalence in register data for those individuals who participated in the survey. Register data was examined for the previous five years and was obtained from the Care Register for Health Care and medical registers, depending on availability of data. Weighted prevalences were calculated taking into account the sampling design and non-response. Results For all examined risk factors and diseases, the prevalence was higher in survey data than in register data. The difference was the greatest for elevated blood pressure: the prevalence in survey data was 48.6% (95% CI 46.5, 50.7) versus 17.3% (15.6, 19.0) in register data. The prevalence of diabetes was 11.5% (10.4, 12.5) in survey and 6.9% (5.9, 7.9) in register data. For sleep apnea, the prevalence in survey data was 8.4% (7.4, 9.3) and 4.4% (3.7, 5.1) in register data. Conclusions Register-based information provides more reliable prevalences on diseases needing urgent treatment. Survey data brings out also previously unidentified diseases cases that do not show in registers. Key messages • Register data is cost-effective in health monitoring, but surveys are needed for monitoring risk factors. • Utilizing diverse data sources enhances getting reliable information to support decision-making.

Impact of selective reporting bias on stroke trials: potential compromise in evidence synthesis - A cross-sectional study

BackgroundAccurate reporting of outcomes is crucial for interpreting the results of randomized controlled trials (RCTs). However, selectively reporting outcomes in publications to achieve researchers’ anticipated results still occurs frequently. This study aims to investigate the prevalence of selective reporting of outcomes in RCTs on treating acute ischemic stroke (AIS), identify factors contributing to this issue, and assess its potential impact on the degree and direction of intervention effect.MethodsA search was conducted in MEDLINE, Embase, and the Cochrane Library to collect interventional RCTs on AIS published from 2020 to 2022. Full texts of RCTs were reviewed, and only those reporting International Clinical Trials Registry Platform primary registry numbers were included. Registration information of the RCTs was extracted from the registry platforms and compared with the publications’ details to assess the selective reporting of outcomes. Bayesian multilevel logistic regression was used to analyze the reasons behind selective reporting.ResultsAmong the total of 159 AIS RCTs identified, 82 (51.6%) were ultimately included, as they reported registration numbers, which encompassed 819 outcomes. Among them, 72 RCTs (87.8%) and 497 outcomes (60.7%) exhibited selective reporting. Omission-type selective reporting (downgrading, omitting, or ambiguously reporting) accounted for 36.4%, while addition-type selective reporting (upgrading, adding, or altering the measurement scope of outcomes) comprised 63.6%. Omission-type selective reporting correlated with negative results (OR: 7.39; 95% CI: 4.08—13.44), whereas addition-type selective reporting correlated with positive results (OR: 2.07; 95% CI: 1.34—3.26) and publication in journals that are not in the top quartile of the Journal Citation Reports (OR: 2.48; 95% CI: 1.15—5.38).ConclusionsRegistered interventional AIS RCTs still face significant issues regarding selective reporting of outcomes. Therefore, it is necessary to further evaluate the influence of selective reporting bias on the positive results obtained from individual AIS RCTs and the systematic reviews based on these RCTs.

The importance of healthcare monitoring systems

Abstract Issue Healthcare evaluation is an important tool of governance in the health sector that reinforces its key components: evidence-based care, effectiveness, programme standards, performance monitoring, quality improvement, risk and incident management and patient-centered care. One of the evaluation systems used by the Veneto Region (north italian region of about 4.8 million inhabitants) consists of the National Outcomes Programme (PNE) developed by the Italian National Agency for Regional Healthcare Services (AGENAS) on behalf of the Health Ministry. Description The PNE is an instrument used to measure, analyze, evaluate and monitor clinical and welfare assistance performances of Italian healthcare facilities. The Veneto Region, in particular, utilizes a regional epidemiologic service to periodically monitor 21 performance indicators based on “treemap” methodology. These indicators are used to assess processes, outcomes and volumes. The data used to monitor these practices derive from the hospital discharge documentation (SDO) and tax registry information system. The aim of this monitoring system is to track any critical situation that is not in alignment with national guidelines and to improve the efficacy, efficiency, appropriateness and safety within the healthcare system. Results In the Veneto region, over the course of nearly 10 years, this method of management has led to the development of various training courses that guide clinicians to correctly document and code different diagnosis and treatments. The treemap process of analysis has also made possible various clinical audits that have provided significant policy changes regarding risk and overall health management. Lessons Having a constant view of healthcare performance has proven to be helpful not only to identify some common gaps in care given from different facilities on a national level, but also guides different regions to develop specific strategies that lead to quality improvement. Key messages • Data on healthcare quality can be used as comparative information and for quality improvement. • Continuous monitoring of specific performance indicators can help identify difficulties in healthcare facilities and create strategies for long term solutions.

Sociodemographic disparities in depression and anxiety symptom severity among young adults in Sweden

Abstract Background Young adults are disproportionately affected by mental health problems such as depression and anxiety. While previous studies have identified sociodemographic predictors using the four-item Patient Health Questionnaire (PHQ-4), differences in symptom severity remain less explored. This study aimed to evaluate the sociodemographic predictors of different levels of depression and anxiety symptoms among young adults in Sweden. Methods Survey data from a population-based sample of Swedish adults aged 20-21 (n = 3,226) was used. Symptom severity was categorized into three levels: normal/mild, moderate, and severe, based on established cutoff scores on the PHQ-4. Gender, parental education and birth country, and upper secondary education were assessed through linked registry information. Multinomial logistic regression was used. Results Females had a higher risk of moderate (RR = 1.8, 95% CI 1.5, 2.2) and severe (RR = 2.3, 95% CI 1.8, 3.1) symptoms (compared to no or mild symptoms) relative to males. Among males, the risk of moderate symptoms was higher without a secondary degree (RR = 1.9, 95% CI 1.0, 3.4) compared to an academic degree. Males with parents born abroad faced elevated risks of moderate (RR = 1.9, 95% CI 1.3, 2.9) and severe (RR = 2.2, 95% CI 1.3, 3.7) symptoms relative to males with Swedish-born parents. Among females, parental tertiary education was associated with a lower risk of moderate symptoms (RR = 0.7, 95% CI 0.5, 0.9) compared to lower education. Further, females having a vocational (RR = 1.5, 95% CI 1.1, 2.1) or no secondary degree (RR = 3.6, 95% CI 2.1, 6.1) had an increased risk of severe symptoms compared to females with an academic degree. Conclusions This study identified sociodemographic predictors for moderate and severe symptoms of depression and anxiety among young men and women in Sweden. Mental health promotion and prevention should specifically target young adults at elevated risk of experiencing moderate to severe symptoms. Key messages • Understanding sociodemographic predictors of depression and anxiety symptoms in young adulthood is essential for public health work. • Mental health promotion and prevention needs to include appropriate interventions for young men and women from diverse socioeconomic and cultural backgrounds.

Weekend compensatory sleep is associated with reduced risk of heart disease: a prospective UK Biobank-based cohort study

Abstract Background and Aims Influenced by the demands of school or work, people often fall out of sync with their circadian rhythms, which leads to sleeping in on days off. Despite this phenomenon, there is a lack of research examining whether compensatory weekend sleep has positive effects on heart health. Methods and results Our prospective cohort study utilized data from the UK Biobank (UKB) and encompassed 90,903 participants. To evaluate the relationship between compensated weekend sleep and heart disease. The sleep data was recorded by Axivity AX3 accelerometer from UKB and grouped by quartiles. Hospitalization records and cause of death registry information were used to diagnose various cardiac diseases including IHD, HF, AF, and stroke. Then multivariate Cox proportional hazard modeling was used to examine the potential impact of compensated sleep on the risk of heart disease. At a median follow-up time of up to 13.8 years, we found that participants in the compensatory sleep group(Q4) had a lower risk of having heart disease in the fully adjusted model (HR: 0.809, 95%CI: 0.766,0.854), which was not influenced by the effect of genetic risk for IHD, HF, or AF. These relationships were even more pronounced in the subgroup with daily sleep deprivation (HR:0.801, 95%CI: 0.718,0.894). Conclusion Sufficient compensatory sleep on weekends is linked to a low risk of heart disease, including IHD, AF, HF, and stroke, which is independent of genetic risk. And the association becomes even more pronounced among individuals who regularly experience inadequate sleep on weekdays.Visual abstract

Paracetamol and adverse cardiovascular outcomes in patients with heart failure

Abstract Background Paracetamol is the most widely used pain killer worldwide, yet its potential influence on cardiovascular events among patients with heart failure (HF) remains unclear. Thus, this study aims to investigate both the short-term and long-term effects of paracetamol use on all-cause mortality and cardiovascular events in HF patients. Methods Using the previously validated territory-wide clinical information registry, paracetamol use was identified among all eligible patients with HF (N = 211,409) from 2000 to 2020. A case-crossover design was used to investigate the associations between short-term use of paracetamols and all-cause mortality and cardiovascular outcomes. Defined daily dose (DDD) was used to evaluate the dose-response relationship. Additionally, the marginal structural design was applied to model paracetamol use as a time-varying exposure and evaluate its long-term effects during the follow-up period. Results Of all eligible subjects, the mean age was 73.2±12.3 years, and 43.6% was male. Over a median follow-up of 10.5 years (interquartile range: 5.7-15.8), 113,258 (53.6%) patients were re-hospitalised for HF, HF-related death occurred in 11,892 (5.6%) patients, incident myocardial infarction (MI) in 28223 (13.3%) patients, incident stroke in 26458 (12.5%) patients, cardiovascular death (CVD) in 72,175 (34.1%) patients and all-cause death in 123,505 (58.4%) patients. Associating HF rehospitalisation to previous paracetamol exposure using the case-crossover design displayed an elevated risk (OR 3.62, 95% CI 3.53-3.71). Associations were similar for MI (OR 5.77, 95% CI 5.50-6.05), stroke (OR 2.37, 95% CI 2.25-2.49), HF-related death (OR 1.86, 95% CI 1.75-1.98), CVD (OR 3.64, 95% CI 3.55-3.73), and all-cause mortality (OR 3.32, 95% CI 3.26-3.39). Moreover, compared with paracetamol users taking the reference dose, users of low, medium and high doses of paracetamol had 16%, 17% and 52% higher risk of HF rehospitalisation. During the long-term follow-up, the average weighted hazard ratio (HR) for HF rehospitalisation in paracetamol users was 2.48 (95% CI 2.46–2.50) as compared with paracetamol non-users after accounting for all-cause mortality as a competing risk. Similarly, the average weighted HR for incident MI or stroke was 2.71 (2.62-2.81) and 3.45 (3.34-3.57), respectively. However, neutral results were shown for HF-related death, CVD and all-cause mortality. Conclusion The use of paracetamol among patients with HF is common. Paracetamol was associated with adverse cardiovascular outcomes both in short-term and long-term follow-up with a dose-response relationship, while its impact on long-term mortality remained neutral. Future randomized controlled trials or experimental studies are warranted to gain a comprehensive understanding of the underlying mechanisms driving such associations between paracetamol and adverse cardiovascular outcomes.

Long-term safety and efficacy of MBA-P01 for the treatment of glabellar lines: results from a multicenter, repeated-dose, open-label extension study

Purpose Following the introduction of new type of botulinum toxin (MBA-P01), a recent phase 3 study demonstrated that MBA-P01 showed comparable efficacy and safety to onabotulinumtoxin A for reducing glabellar lines. The primary objective of this study was to evaluate the long-term safety of repeated MBA-P01 administration for the treatment of glabellar lines. Materials and methods This multicenter, single-group, repeated-dose, long-term open-label extension study evaluated repeated treatment with MBA-P01 (20 U, five treatments over 16 months), with posttreatment evaluation performed up to 52 weeks. Results Based on the safety assessment results, no specific irreversible adverse reactions were associated with the safety profile of MBA-P01. Repeated treatment with MBA-P01 was effective for a treatment duration of 3–5 months. Conclusion In conclusion, multiple cycles of treatment of glabellar lines with MBA-P01 at a dose of 20 U were well tolerated. Clinical trial registration information: This study is registered in ClincalTrials.gov (NCT05321979).

Automated Weld Path Generation in Cluttered Environments using Segmentation and Iterative Closest Point Workpiece Localization

Abstract A significant amount of manufacturing is performed by small to medium enterprises (SMEs), but these manufacturers often have lower adoption rates of automation. The cost and complexity associated with traditional robot systems slows the adoption of robotic welding operations for SMEs. The recent increase in collaborative robot (cobot) welding systems is bridging the gap however, by reducing the complexity of installing, maintaining and training operators to perform weld operations with cobots. These systems add flexibility in range of operator use and ease of deployment. These systems however still rely on kinematic registration between the robot-mounted torch and workpiece on any open-loop weld. This requires precise placement of the workpiece prior to preforming a weld. This work will discuss a method for part identification and registration in a welding task as a step toward automated weld path generation. The method is based on lower-resolution 3D cameras (RGBD cameras) using a combination of color and depth information. This information is used to both identify the workpiece within a workspace that may have other, non-workpiece items, and then provide registration or localization information of the workpiece within a resolution that could allow follow-on near-position strategies to achieve final weld-path identification.

Epidemiological characteristics of patients with juvenile dermatomyositis in China: a multicenter study.

Juvenile dermatomyositis (JDM) is a rare but chronic autoimmune disease with systemic nonsuppurative inflammation. Many studies have focused on the clinical characteristics and therapy of JDM. A few studies have reported the epidemiological characteristics and social burden of JDM patients abroad, but there has been no study in China. This study was based on the FUTang Updating medical REcords Database. Data was extracted from the registry information of inpatient medical records. The epidemiological characteristics and economic burden of Chinese JDM patients were analyzed. A total of 1164 JDM patients from 24 hospitals were enrolled from Jan 2016 to Dec 2021. The ratio of boys to girls was 1:1.23, and half were between 6 and 12 years old. Over half(n=629) were admitted to the hospital at least twice for intensive treatment. 20% of JDM patients suffered from lung involvement and 2.2% suffered from subcutaneous calcification. The median days of hospitalization were 10 (range 6 to 14), in addition, the median United States dollar (USD) of expense was 2370.5(1373.7,3541.9). Lung involvement was the major factor causing high inpatient burden, length of stay, and expense. Nearly, 17% of JDM patients were admitted to the hospital as emergencies, suggesting sever disease activity stage needing urgent treatment. No deaths occurred in our study. Our study documents the epidemiological characteristics and social burden of JDM patients in China, contributing to the enhanced comprehension and effective management of JDM in the country.

Mapping refugee populations at high resolution by unlocking humanitarian administrative data

BackgroundInforming local decision-making, improving service delivery and designing household surveys require having access to high-spatial resolution mapping of the targeted population. However, this detailed spatial information remains unavailable for specific population subgroups, such as refugees, a vulnerable group that would significantly benefit from focused interventions. Given the continuous increase in the number of refugees, reaching an all-time high of 35.3 million people in 2022, it is imperative to develop models that can accurately inform about their spatial locations, enabling better and more tailored assistance.MethodsWe leverage routinely collected registration data on refugees and combine it with high-resolution population maps, satellite imagery derived settlement maps and other spatial covariates to disaggregate observed refugee totals into 100-m grid cells. We suggest a deterministic grid cell allocation inside monitored refugee sites based on building count and a random-forest-derived grid cell allocation outside refugee sites based on geolocating the textual geographic information in the refugee register and on high-resolution population mapping. We test the method in Cameroon using the registration database monitored by the United Nations High Commissioner for Refugees.ResultsUsing OpenStreetMap, 83% of the manually inputted information in the registration database could be geolocated. The building footprint layer derived from satellite imagery by Ecopia AI offers extensive coverage within monitored refugee sites, although manual digitization was still required in rapidly evolving settings. The high-resolution mapping of refugees on a 100-m grid basis provides an unparalleled level of spatial detail, enabling valuable geospatial insights for informed local decision-making.ConclusionsGathering information on forcibly displaced persons in sparse data-setting environment can quickly become very costly. Therefore, it is critical to gain the most knowledge from operational data that is frequently collected, such as registration databases. Integrating it with ancillary information derived from satellite imagery paves the way for obtaining more timely and spatially precise information to better deliver services and enhance sampling frame for target data collection exercises that further improves the quality of information on people in need.

Many randomized trials in a large systematic review were not registered and had evidence of selective outcome reporting: a meta-epidemiological study

ObjectivesThe primary objectives were to describe characteristics of trial registration in the chronic low back pain (CLBP) field and assess the association of trial registration status (registered vs. unregistered, prospectively registered vs. retrospectively registered) with risk of bias, sufficient sample size, quality of reporting, and treatment effect estimates. Secondary objectives were to describe trial registration consistency with the final report and assess its association with risk of bias, sufficient sample size, and treatment effect estimates. Study design and settingA cross-sectional meta-epidemiological study of trials included in a large Cochrane review on exercise treatments for CLBP. We extracted relevant trial and registration information and assessed trials’ risk of bias using the Cochrane RoB 1 tool. We performed descriptive analyses, logistic regressions, and subgroup meta-analyses. ResultsWe included 361 trials, of which 23.3% were prospectively registered. Registered trials had lower risk of bias (OR 0.6; 95%CI 0.5, 0.7) and higher reporting quality (OR 1.6; 95%CI 1.4, 1.8) than unregistered trials. Prospectively registered trials were more likely to have low risk of reporting bias (OR 2.7; 95%CI 1.2, 6.5) and higher quality of reporting (OR 1.3; 95%CI 1.1, 1.6) than retrospectively registered trials. Trial registration status was not associated with effect estimates. Among prospectively registered trials, 64.3% clearly defined primary outcome(s) in their registration, 58.3% had consistent sample sizes, and 22.6% had no evidence of selective outcome reporting. Trials that clearly defined primary outcome(s) were more likely to report larger effect estimates for pain intensity (MD -15.8; 95%CI -22.7, -8.9 versus -6.0; 95%CI -10.6, -1.5; Q=6.7, p=0.01), although the difference was small, the 95%CIs overlapped, and no difference was found for functional limitations. ConclusionA small proportion of trials in the CLBP field were registered prospectively and many presented registration inconsistencies. Registered trials tend to have lower risk of bias and higher quality of reporting. Policies are needed to improve prospective registration and registration consistency in the field.

Evaluating Compliance of Randomized Controlled Trial Abstracts in Plastic Surgery Journals with CONSORT Guidelines Using GPT-4 AI

Background: The quality of reporting in randomized controlled trials (RCTs) is crucial for accurate interpretation and synthesis of evidence. The Consolidated Standards of Reporting Trials (CONSORT) guidelines provide a standardized framework for reporting RCT abstracts. This study aimed to evaluate the adherence of RCT abstracts published in three major plastic surgery journals to the CONSORT tool guideline for reporting abstracts, utilizing Generative Pre-trained Transformer 4 artificial intelligence (GPT-4 AI) technology. Methods: Abstracts of RCTs published between 2010 and 2023 were collected. The GPT-4 AI model was utilized to assess the abstracts based on the CONSORT criteria. Descriptive statistics were used to report the compliance scores and identify areas where abstracts lacked compliance. Results: Of the initially identified 500 abstracts, a total of 371 RCT abstracts met the inclusion criteria and were analyzed. The mean CONSORT score was 10.05 (±2.22), with a median score of 10.72. Specific areas where abstracts lacked compliance included trial design (39.6%), participant details (28.8%), intervention descriptions (15.6%), randomization process (25.3%), and the number of participants analyzed (33.4%). Trial registration (18.3%) and funding information (15.1%) were also frequently missing. Conclusions: Our study’s innovative use of the GPT-4 AI model for analysis demonstrated the potential of AI technology in streamlining and enhancing the evaluation of research compliance. We advocate for heightened awareness and more rigorous application of CONSORT guidelines among authors, reviewers, and journal editors. Emphasizing the role of AI technology in the evaluative process can further improve the reporting quality of future RCTs in plastic surgery, contributing to more reliable and transparent research in the field.

Achieving accurate prostate auto-segmentation on CT in the absence of MR imaging

BackgroundMagnetic resonance imaging (MRI) is considered the gold standard for prostate segmentation. Computed tomography (CT)-based segmentation is prone to observer bias, potentially overestimating the prostate volume by ∼ 30 % compared to MRI. However, MRI accessibility is challenging for patients with contraindications or in rural areas globally with limited clinical resources. PurposeThis study investigates the possibility of achieving MRI-level prostate auto-segmentation accuracy using CT-only input via a deep learning (DL) model trained with CT-MRI registered segmentation. Methods and MaterialsA cohort of 111 definitive prostate radiotherapy patients with both CT and MRI images was retrospectively grouped into training (n = 37) and validation (n = 20) (where reference contours were derived from CT-MRI registration), and testing (n = 54) sets. Two commercial DL models were benchmarked against the reference contours in the training and validation sets. A custom DL model was incrementally retrained using the training dataset, quantitatively evaluated on the validation dataset, and qualitatively assessed by two different physician groups on the validation and testing datasets. A contour quality assurance (QA) model, established from the proposed model on the validation dataset, was applied to the test group to identify potential errors, confirmed by human visual inspection. ResultsTwo commercial models exhibited large deviations in the prostate apex with CT-only input (median: 0.77/0.78 for Dice similarity coefficient (DSC), and 0.80 cm/0.83 cm for 95 % directed Hausdorff Distance (HD95), respectively). The proposed model demonstrated superior geometric similarity compared to commercial models, particularly in the apex region, with improvements of 0.05/0.17 cm and 0.06/0.25 cm in median DSC/HD95, respectively. Physician evaluation on MRI-CT registration data rated 69 %-78 % of the proposed model’s contours as clinically acceptable without modifications. Additionally, 73 % of cases flagged by the contour quality assurance (QA) model were confirmed via visual inspection. ConclusionsThe proposed incremental learning strategy based on CT-MRI registration information enhances prostate segmentation accuracy when MRI availability is limited clinically.

Comparison of basal ganglia regions across murine brain atlases using metadata models and the Waxholm Space

The murine basal ganglia regions are targets for research into complex brain functions such as motor control and habit formation. However, there are several ways to name and annotate these regions, posing challenges for interpretation and comparison of data across studies. Here, we give an overview of basal ganglia terms and boundaries in the literature and reference atlases, and describe the criteria used for annotating these regions in the Waxholm Space rat brain atlas. We go on to compare basal ganglia annotations in stereotaxic rat brain atlases and the Allen Mouse brain Common Coordinate Framework to those in the Waxholm Space rat brain atlas. We demonstrate and describe considerable differences in the terms and boundaries of most basal ganglia regions across atlases and their versions. We also register information about atlases and regions in the openMINDS metadata framework, facilitating integration of data in neuroscience databases. The comparisons of terms and boundaries across rat and mouse atlases support analysis and interpretation of existing and new data from the basal ganglia.

Pulp calcification identification on cone beam computed tomography: an artificial intelligence pilot study

BackgroundThis study aims to verify the effectiveness of a deep neural network (DNN) in automatically identifying pulp calcification on cone beam computed tomography (CBCT) images.MethodsThis study retrospectively analysed 150 CBCT images. Pulp calcification was identified and manually annotated by three endodontists with 10 years of experience. A DNN model based on the U-Net architecture was constructed to identify pulp calcification, and ten rounds of fourfold cross-validation were conducted. The model performance was evaluated using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC).ResultsThe model achieved a sensitivity of 75.91 ± 2.84% in automatically identifying pulp calcification, with a specificity of 68.88 ± 2.35%, an accuracy of 72.78 ± 2.13%, and an AUC of 73.68 ± 3.09%. According to the ranking for diagnostic tests, the proposed method achieved a “good” grade for sensitivity, accuracy, and AUC and a “fair” grade for specificity.ConclusionsThe results indicate that the proposed method shows promise for identifying pulp calcification on CBCT images. Future research aims to expand the dataset and refine the model, thereby enhancing its clinical applicability. The integration of artificial intelligence into diagnostic and treatment systems is anticipated to increase the efficiency of diagnosing pulp calcification and assist dentists in assessing the difficulty of root canal treatment cases preoperatively.Clinical registrationRegistration was performed on the Chinese Clinical Trial Registry (https://www.chictr.org.cn/) (Registration number: ChiCTR2300077078, 27/10/2023) and National Medical Research Registry Information System (https://www.medicalresearch.org.cn/, 30/10/2023) (Number: MR-44–23-039664).

Suicidal Ideation, Clinical Worsening and Outcomes among Child Participants in Trauma-Focused Treatment.

Research on clinical trajectories of children with suicidal ideation (SI) and clinical worsening (CW) during cognitive behavioral therapy (CBT) for childhood trauma is limited. The current study utilized secondary data from a randomized clinical trial comparing Trauma-Focused CBT versus Stepped care CBT to examine (1) clinical profile and outcomes of children with SI, recurrent thoughts of death/dying, and past attempts; and (2) clinical worsening during treatment and difference in outcomes. Children (ages 4-12) and their parents (N = 183) participated and 132 completed treatment. Interviews assessing SI, thoughts of death/dying, past attempts, and number/type of trauma were conducted with children (ages 7-12) and parents. Outcome measures (baseline and post-treatment) completed by parents included child posttraumatic stress symptoms, impairment, and internalizing and externalizing problems and an independent evaluator rated overall severity. Children (ages 7-12), parents and therapists completed ratings during treatment that indicated CW. Results did not differ by treatment group; thus, the pooled sample was used. Children with SI at baseline had higher severity than children without. For completers, no children (n = 14) with SI at baseline had SI at post-treatment. Child (n = 3) and therapist (n = 5) ratings of CW were low. Eighteen children (13.64%) were rated as CW by parents. Most ratings occurred at the beginning of treatment. Children with therapist ratings of CW had higher internalizing problems. Findings suggest that children with SI and CW can improve from trauma-focused CBT. More research is needed on the clinical trajectory of children with SI and trauma, and on clinical tools to monitor CW. Clinical trial registration information: https://clinicaltrials.gov : NCT02537678.

Internship Information System at PT Biro Klasifikasi Indonesia Samarinda Branch

The rapid development of information technology has led almost all companies and certain sectors to desire that all processes in their offices become fast and practical, including the internship application and registration process. PT Biro Klasifikasi Indonesia, Samarinda Branch, is a company that utilizes information technology; however, there are still aspects of information technology use that are not yet effective and efficient. One such aspect is the student internship registration, which is still done by registering directly at the company. This process is quite time-consuming and has many weaknesses. These weaknesses include long processing times and inadequate accuracy, such as incomplete or lost documents upon receipt, which can disadvantage applicants. As a result, this registration system poses difficulties for prospective interns outside the area. Therefore, the purpose of designing and developing a web-based internship information system at PT Biro Klasifikasi Indonesia Samarinda Branch is to simplify the internship registration process and assist the admin in verifying registration status, managing certificate data, and making it easier for applicants to fill out the registration form, view registration results, and check registration status through the website. The system development method used in this research is the waterfall model. To implement this internship registration information system, supporting components must function properly. These components use the Laravel framework and MySQL database. The results of this research, based on black box testing and respondent questionnaires, indicate that all features of this web-based internship information system can be used effectively and function as intended, thereby helping users manage internship registration.

Biometric access system with automatic registration and loyalty check in the system of recognition and database entry

ABSTRACT Against the backdrop of ongoing terrorist threats and uncontrolled migration flows, there is increased interest in biometric human recognition technology and barcoding technology combined with it. As a possible solution, a biometric access system is proposed, designed to implement three main urgent tasks – automatic registration of people crossing the border; recording registration information in a special biometric database; checking registration information and facial image on the database of wanted people. Effective biometric systems combine verification and identification tasks to increase the reliability of person identification and comparison with reference data sets. In the course of the study, various methods of information processing with its subsequent encoding were considered and analyzed, and the experience of previous systems of this type was investigated. As a result of the study, the effectiveness of using the latest biometric technologies in the migration sphere and in law enforcement agencies for the effective search of offenders has been substantiated. This paper will be useful as a material for further study of facial recognition technology and the integration of QR into the field of biometrics.

Modelling Residential Fire Vulnerability of Denmark

AbstractThis study analyzes socio-economic demographics (including Geomatic conzoom® segmented demographic variables) as well as building and property registration information as risk factors in relation to the prevalence of residential building fires within 100 m × 100 m cells. The logistic regression model achieved a receiver operating curve (ROC) of 0.74 and a precision-recall curve of 0.12 on the testing dataset. The model identifies 19 significant variables related to the risk of residential fire. The top 5 highest performing variables in our model and their odds ratios are the following: number of people (OR 1.25), Multi/family residence-building type (OR 1.20), number of buildings (OR 1.18), conzoom® Type C—Country/Rural Communities (OR 0.85), construction year (OR 0.87). These results indicate that socio-economic factors play a large role in influencing fire vulnerability within residential areas and can help prioritize resource allocation to reduce fire vulnerability in the identified risk factor groups.

Learning Discriminative Features via Multi-Hierarchical Mutual Information for Unsupervised Point Cloud Registration

Learning Discriminative Features via Multi-Hierarchical Mutual Information for Unsupervised Point Cloud Registration