Risk factors for severe and fatal childhood unintentional injury: a systematic review protocol.

BACKGROUND: Unintentional injuries are a leading cause of death among children aged 1-19 years worldwide. Systematic reviews assessing various risk factors for different childhood injuries have been published previously. However, most of the related literature does not distinguish minor from severe or fatal injuries. This study aims to describe and summarize the current knowledge on the determinants of severe and fatal childhood unintentional injuries and to discuss the differences between risk factors for all injuries (including minor injuries) and severe and fatal injuries. The study also aims to quantify the reduction in childhood injuries associated with a reduction in exposure to some of the identified risk factors in the Canadian population. METHODS: A systematic review and meta-analysis will be conducted by searching MEDLINE, Embase, CINAHL, and Web of Science. Observational and experimental cohort studies assessing children and adolescents aged ≤ 19 years old and determinants of severe and fatal unintentional injury, such as personal behaviors, family and environmental characteristics, and socioeconomic and geographic context, will be eligible. The main outcome will be a composite of any severe or fatal unintentional injuries (including burns, drowning, transport-related injuries, and falls). Any severity measurement scale will be accepted as long as severe cases require at least one hospital admission. Two authors will independently screen for inclusion, extract data, and assess the quality of the data using the Cochrane ROBINS-E tool. Meta-analysis will be performed using random effects models. Subgroup analyses will examine age subgroups and high- vs low-income countries. Sensitivity analysis will be conducted after restricting analyses to studies with a low risk of bias. Attributable fractions will be computed to assess the burden of identified risk factors in the Canadian population. DISCUSSION: Given the numerous determinants of childhood injuries and the challenges that may be involved in identifying which individuals should be prioritized for injury prevention efforts, this evidence may help to inform the identification of high-risk children and prevention interventions, considering the disproportionate consequences of severe and fatal injuries. This evidence may also help pediatric healthcare providers prioritize counseling messaging. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42023493322.

Epidemiological characteristics and factors affecting healing in unintentional pediatric wounds.

Objective: To analyze the epidemiological characteristics and wound healing conditions of common unintentional skin lacerations in children. Methods: A retrospective analysis was conducted on data from 1,107 children, aged 0-12 years, with skin lacerations who received emergency treatment at Qilu Hospital of Shandong University from January 1, 2019, to December 30, 2022. Data on age, injury site, time from injury to suturing, and wound healing conditions were statistically analyzed. Results: Among the 1,107 cases, 714 (64.5%) were male and 393 (35.5%) were female, with a male-to-female ratio of 1.8:1; median age was 5 years (IQR, 3-7). Infants and toddlers (0-3 years old) constituted the highest proportion, accounting for 36.3% (402 cases). The number of children aged over 3 years gradually decreased with increasing age. In younger children, the most common injuries were to the forehead, scalp, and lower jaw; in school-aged children, the proportion of limb and trunk injuries significantly increased. Age (OR, 1.34; 95% CI, 1.23-1.46), outdoor injuries (OR, 2.21; 95% CI, 1.18-4.16), lower limb injuries (OR, 5.35; 95% CI, 2.86-10.00), and wound length greater than 3 cm (OR, 10.65; 95% CI, 5.02-22.60) were significant risk factors for poor wound healing. The risk of poor wound healing increased by 34% for each additional year of age. Conclusion: In children, the common sites of unintentional skin lacerations show distinct age and gender distribution characteristics. Older age, outdoor injuries, longer wound lengths, and lower limb injuries are independent risk factors for poor wound healing.

Analysis of factors influencing the degree of accidental injury of bicycle riders considering data heterogeneity and imbalance.

Bicycle safety has emerged as a pressing concern within the vulnerable transportation community. Numerous studies have been conducted to identify the significant factors that contribute to the severity of cyclist injuries, yet the findings have been subject to uncertainty due to unobserved heterogeneity and class imbalance. This research aims to address these issues by developing a model to examine the impact of key factors on cyclist injury severity, accounting for data heterogeneity and imbalance. To incorporate unobserved heterogeneity, a total of 3,895 bicycle accidents were categorized into three homogeneous sub-accident clusters using Latent Class Cluster Analysis (LCA). Additionally, five over-sampling techniques were employed to mitigate the effects of data imbalance in each accident cluster category. Subsequently, Bayesian Network (BN) structure learning algorithms were utilized to construct 32 BN models after pairing the accident data from the four accident cluster types before and after sampling. The optimal BN models for each accident cluster type provided insights into the key factors associated with cyclist injury severity. The results indicate that the key factors influencing serious cyclist injuries vary heterogeneously across different accident clusters. Female cyclists, adverse weather conditions such as rain and snow, and off-peak periods were identified as key factors in several subclasses of accident clusters. Conversely, factors such as the week of the accident, characteristics of the trafficway, the season, drivers failing to yield to the right-of-way, distracted cyclists, and years of driving experience were found to be key factors in only one subcluster of accident clusters. Additionally, factors such as the time of the crash, gender of the cyclist, and weather conditions exhibit varying levels of heterogeneity across different accident clusters, and in some cases, exhibit opposing effects.

Epidemiology of Accidental Injuries at Home and Related Risk Factors for Mortality among Older Adults in South Korea: A Retrospective Cohort Study.

Background and Objectives: Accidental home injuries among older adults are increasing globally, but reporting is limited. This study aims to establish foundational data for program development and policies to prevent accidental injuries at home in older adults by using data on the occurrence of accidental injuries at home and analyzing the risk factors of mortality due to accidental injuries among adults aged 65 years and older. Materials and Methods: This retrospective study used data from the community-based Severe Trauma Survey in South Korea. This study identified general, injury-related, and treatment-related characteristics of older adults who were transported to the emergency department with accidental injuries at home. Single-variable and multiple logistic regression analyses were used to identify risk factors for mortality after injury. Results: The majority of older adults in this study who experienced accidental injuries at home were aged 75 to 84 (42.8%) and female (52.8%), with 1465 injured from falls and slips (68.0%). Risk factors for mortality included older age (≥85 years) (ORs 2.25, 95% CI 1.47-3.45), male sex (ORs 1.60, 95% CI 1.15-2.20), mechanism of injury (falls or slips vs. contact injury, ORs 6.76, 95% CI 3.39-13.47; airway obstruction vs. contact injury, ORs 13.96, 95% CI 6.35-30.71), higher severity (moderate vs. mild, ORs 2.56, 95% CI 1.45-4.54; severe vs. mild, ORs 12.24, 95% CI 6.48-23.12; very severe vs. mild, ORs 67.95, 95% CI 38.86-118.81), and receiving a blood transfusion (ORs 2.14, 95% CI 1.24-3.67). Conclusions: Based on these findings, the home and community environments where older adults live should be inspected and monitored, and in-home accidental injury prevention strategies should be developed tailored to the characteristics of older adults' risk factors and their injury-related characteristics.

Pediatric and Young Adult Trauma Recidivism.

INTRODUCTION: Unintentional injury is the leading cause of death among children. Much can be gleaned from the adult literature in understanding the characteristics that lead to recidivism in efforts to establish interventions for prevention. Our study aims to evaluate the rates, demographics, and features of pediatric trauma recidivism. METHODS: This was a retrospective single-institution review at a level-1 pediatric trauma center of children and young adults (ages 0-28) with traumatic injuries from January 2008 to April 2023. Patients with 1 or more prior visits to our institution's trauma center (recidivists) were identified and compared with those with single admissions. Chi-square tests were used to statistically analyze the two groups. RESULTS: Pediatric/young adult trauma recidivists were 4.4% of the total trauma population captured (n = 14,613). Of the total trauma group, 55% were under 18 years old. Recidivists had higher percentages of patients who were male (82% vs 69%, P < .01), African American (36% vs 24%, P < .01), involved in penetrating trauma (33% vs 17%, P < .01), self-pay/uninsured (17% vs 12%, P < .01), and have abuse reported (5% vs 4%, P = .04). The primary county for recidivism patients was Forsyth with most patients from a specific zip code in an urban area of the county. The average time between visits for recidivists was 1,066 days. CONCLUSIONS: Pediatric/young adult trauma recidivism is associated with specific characteristics including male, African American race, penetrating trauma, and uninsured status. Recidivists are primarily presenting from a zip code with low socioeconomic status. It is critical to develop targeted interventions to help this population in trauma prevention.

Unintended Consequences: A Case Study of a Toddler's Accidental Burn Injury Caused by a Hair Crimper.

ABSTRACT: Recognizing when an injury is concerning for abuse is of utmost importance, as failure to do so places the child at a significant risk for further inflicted injury. Misinterpreting accidental injury as abuse can also have dire consequences for a child and their caregivers. The provider must be aware of characteristics of cutaneous injuries, including burns, that are concerning for abuse. Understanding these characteristics and putting them into clinical practice is necessary to protect children from additional abuse and protect families from wrongful accusations. This case report evaluates a 12-month-old boy who presented to a pediatric emergency room with a patterned, full-thickness burn to the leg. After evaluation, we summarized the additional medical evaluation needed, including a focused history and physical examination, and recommended appropriate diagnostic testing needed to evaluate a child of this age for possible child maltreatment.

A novel repair strategy using knotless squeeze anchors for lumbar disc herniation with endplate junction lesions under biportal endoscopic spinal surgery.

BACKGROUND: Annulus fibrosus-endplate (AF-EP) junction lesions are important determinants for lumbar disc herniation (LDH). Utilizing biportal endoscopic spinal surgery (BESS), we introduce a novel repair method using bioabsorbable PushLock anchors with suture fibers to stretch disconnected AF tissues to the vertebral cortex. METHODS: The viewing and working portals are established to excise herniated disc materials causing radiculopathy. Through the working portal, a suture strand is passed through the intact AF tissue near the lesion and retrieved using the Suture Crossing Device. Then, the knotless suture limbs are secured into the cortical bone socket of the vertebral body with a PushLock anchor. CONCLUSION: The procedure is a simple, safe, and feasible knotless suturing technique for the treatment of LDH with AF-EP junction lesions.

A study on expression recognition based on improved mobilenetV2 network.

This paper proposes an improved strategy for the MobileNetV2 neural network(I-MobileNetV2) in response to problems such as large parameter quantities in existing deep convolutional neural networks and the shortcomings of the lightweight neural network MobileNetV2 such as easy loss of feature information, poor real-time performance, and low accuracy rate in facial emotion recognition tasks. The network inherits the characteristics of MobilenetV2 depthwise separated convolution, signifying a reduction in computational load while maintaining a lightweight profile. It utilizes a reverse fusion mechanism to retain negative features, which makes the information less likely to be lost. The SELU activation function is used to replace the RELU6 activation function to avoid gradient vanishing. Meanwhile, to improve the feature recognition capability, the channel attention mechanism (Squeeze-and-Excitation Networks (SE-Net)) is integrated into the MobilenetV2 network. Experiments conducted on the facial expression datasets FER2013 and CK + showed that the proposed network model achieved facial expression recognition accuracies of 68.62% and 95.96%, improving upon the MobileNetV2 model by 0.72% and 6.14% respectively, and the parameter count decreased by 83.8%. These results empirically verify the effectiveness of the improvements made to the network model.

Injury Pattern Analysis to Optimize Burn Injury Prevention in the Unhoused Community.

Unhoused patients are an overrepresented group in burn injury, and are a uniquely vulnerable population. Current research focuses on the consequences of homelessness on burn outcomes, with little known about the specific circumstances and behaviors leading to burn injury that may represent specific targets for injury prevention efforts. The burn registry at an urban regional burn center was queried for burn admissions in unhoused adults from 2019 to 2022. Registry data pulled included demographics, urine toxicology, mechanism of injury, and injury subjective history. Subjective injury history was reviewed to determine more specific injury circumstances and activities during which accidental burns occurred. Demographic and mechanistic trends in burn admissions were explored via descriptive statistics. Among 254 admissions for burns from the unhoused community, 58.1% of patients were positive for stimulants on admission. Among accidental injuries (69.7%), common circumstances included preparing food or beverages, cooking or using methamphetamine, smoking cannabis or tobacco, bonfires, and candles. A specific common circumstance was lighting a cigarette while handling accelerants (6.7%). Interventions for stimulant abuse, as well as outreach efforts to educate unhoused patients about situational awareness, safe handling of accelerants, safe smoking practices, and safe cooking practices, may be effective tools in reducing burn admissions in this vulnerable population.

Crossbow Injuries: Predictors of Mortality.

INTRODUCTION: Crossbow injuries are rare but carry significant morbidity and mortality, and there is limited evidence in the medical literature to guide care. This paper reviews the case reports and case series of crossbow injuries and looks for trends regarding morbidity and mortality based on the type of arrow, anatomic location of injury, and intent of injury. METHODS: Multiple databases were searched for cases of crossbow injuries and data were abstracted into a spreadsheet. Statistics were done in SPSS. RESULTS: 358 manuscripts were returned in the search. After deduplication and removal of nonclinical articles, 101 manuscripts remained. Seventy-one articles describing 90 incidents met the inclusion criteria. The mean age was 36.5 years. There were 10 female and 79 male victims. Fatality was 36% for injuries by field tip arrows and 71% for broadhead arrows, p = .024. Assaults were fatal in 84% of cases, suicides in 29%, and accidental injuries in 17%, p < .001. Mortality was similar for wounds to the head and neck (41%), chest (42%), abdomen (33%), extremities (50%), and multiple regions, p = .618. CONCLUSIONS: Crossbows are potentially lethal weapons sold with fewer restrictions than firearms. Injuries caused by broadhead arrows are more likely to be fatal than injuries from field tip arrows. The anatomic location of injury does not correlate with fatality. More than half of crossbow injuries are due to attempted suicide, with a high case-fatality rate.

Reliability in distinguishing murder from accidental event with current forensic criteria in traumatic head injury.

Discovering a body displaying signs of multiple head trauma requires a thorough examination by the forensic pathologist, and a multidisciplinary approach is recommended. However, determining the manner of death is not always possible. We present a case in which the body of a 60-year-old man was discovered lying face down on the floor of his apartment, partially unclothed, surrounded by blood and vomit, and presenting numerous head injuries. The autopsy concluded that the cause of death was a result of post-traumatic brain injury. Nevertheless, applying current criteria made it challenging to ascertain whether the trauma stemmed from an accidental event or an assault.

Longitudinal Associations Between Pain, Risk for Posttraumatic Stress Disorder, Posttraumatic Stress Symptoms, and Pain Characteristics in Children After Unintentional Injury.

OBJECTIVES: Approximately 20% of children demonstrate persistent posttraumatic stress symptoms (PTSS) after unintentional injury, with more severe pain intensity predicting concurrent and later PTSS. Examining additional pain characteristics like pain behaviors, impairment related to pain, and subjective experiences of pain might provide additional insight into the mechanisms that reinforce relationships between risk for posttraumatic stress disorder (PTSD), PTSS, and pain. METHODS: During hospitalization for unintentional injury, the Screening Tool for Predictors of PTSD (STEPP) was administered and the highest pain score was collected. One month later, the Child PTSD Symptom Scale and PROMIS questionnaires assessed PTSS and pain characteristics respectively, including intensity, interference, behaviors, and quality. RESULTS: Correlations between PTSS and PROMIS questionnaires were significant. STEPP predicted future PTSS and all PROMIS questionnaires. The highest pain score predicted future PTSS, as well as pain interference and pain behavior, and did not predict pain intensity and pain quality. When STEPP and highest pain score were combined into a single regression, STEPP and highest pain score predicted future PTSS but only STEPP continued to predict all PROMIS questionnaires. DISCUSSION: PTSD risk significantly predicted PTSS and pain characteristics 1 month later. The highest pain score predicted future PTSS and several pain characteristics but no longer had predictive value for pain-related outcomes when combined with PTSD risk. These results indicate that risk factors for PTSD are stronger predictors than pain-related risk factors in predicting pain outcomes. Addressing PTSD risk, as well as pain intensity during hospitalization, may result in improved outcomes for children with unintentional injury.

Integrated image and location analysis for wound classification: a deep learning approach.

The global burden of acute and chronic wounds presents a compelling case for enhancing wound classification methods, a vital step in diagnosing and determining optimal treatments. Recognizing this need, we introduce an innovative multi-modal network based on a deep convolutional neural network for categorizing wounds into four categories: diabetic, pressure, surgical, and venous ulcers. Our multi-modal network uses wound images and their corresponding body locations for more precise classification. A unique aspect of our methodology is incorporating a body map system that facilitates accurate wound location tagging, improving upon traditional wound image classification techniques. A distinctive feature of our approach is the integration of models such as VGG16, ResNet152, and EfficientNet within a novel architecture. This architecture includes elements like spatial and channel-wise Squeeze-and-Excitation modules, Axial Attention, and an Adaptive Gated Multi-Layer Perceptron, providing a robust foundation for classification. Our multi-modal network was trained and evaluated on two distinct datasets comprising relevant images and corresponding location information. Notably, our proposed network outperformed traditional methods, reaching an accuracy range of 74.79-100% for Region of Interest (ROI) without location classifications, 73.98-100% for ROI with location classifications, and 78.10-100% for whole image classifications. This marks a significant enhancement over previously reported performance metrics in the literature. Our results indicate the potential of our multi-modal network as an effective decision-support tool for wound image classification, paving the way for its application in various clinical contexts.

Why do people get burned? Qualitative analysis of the causes of burns in a large survey of burn survivors.

BACKGROUND: Understanding the national epidemiology of burns is necessary for effective planning of prevention and treatment services. However, epidemiological studies of burns have often focused on short-term and retrospective investigations of a specific group of burned patients in Iran. Therefore, we conduct a comprehensive study from August 2016 to October 2017, in the Burn Research Center of Iran University of Medical Sciences on approximately 1700 hospitalized burn patients at Motahari Hospital to identify the underlying causes of burns. In this study, an open-ended question was asked about how the burn occurred in the patients. The current study was designed and conducted for the qualitatively analyzing of the responses to this question, using the content analysis method, in order to maximize its use in policymaking and prevention. METHODS: Content analysis of written responses to open-ended questions was done as a part of a large questionnaire survey. This survey was conducted by face-to-face interview at a national referral center for burn injuries. RESULTS: Answers from 1595 patients were coded and the underlying causes of injury were categorized into three themes and 43 subthemes. These underlying causes were lack of knowledge and awareness, inappropriate equipment, and inevitable accidents. The underlying cause of lack of knowledge and awareness had the highest proportion in causing burns in both men and women. The most common subthemes in descending order included lack of skills, dangerous actions, improper location of hot liquids, individual mistakes, and improper use of flammable materials. The top five subthemes with the highest average percentage of burn in patients in descending order were deficiency of residential equipment, self-immolation, accidents, inappropriate location of flammable materials, and unsafe kitchen appliances. CONCLUSION: The focus of prevention programs on educating and increasing awareness of individuals, preferably women, is suggested. Educational programs, tailored to occupational standards and workplace and household equipment, are recommended for men in work environments.

Optimizing ensemble U-Net architectures for robust coronary vessel segmentation in angiographic images.

Automated coronary angiography assessment requires precise vessel segmentation, a task complicated by uneven contrast filling and background noise. Our research introduces an ensemble U-Net model, SE-RegUNet, designed to accurately segment coronary vessels using 100 labeled angiographies from angiographic images. SE-RegUNet incorporates RegNet encoders and squeeze-and-excitation blocks to enhance feature extraction. A dual-phase image preprocessing strategy further improves the model's performance, employing unsharp masking and contrast-limited adaptive histogram equalization. Following fivefold cross-validation and Ranger21 optimization, the SE-RegUNet 4GF model emerged as the most effective, evidenced by performance metrics such as a Dice score of 0.72 and an accuracy of 0.97. Its potential for real-world application is highlighted by its ability to process images at 41.6 frames per second. External validation on the DCA1 dataset demonstrated the model's consistent robustness, achieving a Dice score of 0.76 and an accuracy of 0.97. The SE-RegUNet 4GF model's precision in segmenting blood vessels in coronary angiographies showcases its remarkable efficiency and accuracy. However, further development and clinical testing are necessary before it can be routinely implemented in medical practice.

Teen Pregnancy and Risk of Premature Mortality.

Importance: Unintentional injury, suicide, and homicide are leading causes of death among young females. Teen pregnancy may be a marker of adverse life experiences. Objective: To evaluate the risk of premature mortality from 12 years of age onward in association with number of teen pregnancies and age at pregnancy. Design, Setting, and Participants: This population-based cohort study was conducted among all females alive at 12 years of age from April 1, 1991, to March 31, 2021, in Ontario, Canada (the most populous province, which has universal health care and data collection). The study period ended March 31, 2022. Exposures: The main exposure was number of teen pregnancies between 12 and 19 years of age (0, 1, or ≥2). Secondary exposures included how the teen pregnancy ended (birth or miscarriage vs induced abortion) and age at first teen pregnancy. Main Outcomes and Measures: The main outcome was all-cause mortality starting at 12 years of age. Hazard ratios (HRs) were adjusted for year of birth, comorbidities at 9 to 11 years of age, and area-level education, income level, and rurality. Results: Of 2 242 929 teenagers, 163 124 (7.3%) experienced a pregnancy at a median age of 18 years (IQR, 17-19 years). Of those with a teen pregnancy, 60 037 (36.8%) ended in a birth (of which 59 485 [99.1%] were live births), and 106 135 (65.1%) ended in induced abortion. The median age at the end of follow-up was 25 years (IQR, 18-32 years) for those without a teen pregnancy and 31 years (IQR, 25-36 years) for those with a teen pregnancy. There were 6030 deaths (1.9 per 10 000 person-years [95% CI, 1.9-2.0 per 10 000 person-years]) among those without a teen pregnancy, 701 deaths (4.1 per 10 000 person-years [95% CI, 3.8-4.5 per 10 000 person-years]) among those with 1 teen pregnancy, and 345 deaths (6.1 per 10 000 person-years [95% CI, 5.5-6.8 per 10 000 person-years]) among those with 2 or more teen pregnancies; adjusted HRs (AHRs) were 1.51 (95% CI, 1.39-1.63) for those with 1 pregnancy and 2.14 (95% CI, 1.92-2.39) for those with 2 or more pregnancies. Comparing those with vs without a teen pregnancy, the AHR for premature death was 1.25 (95% CI, 1.12-1.40) from noninjury, 2.06 (95% CI, 1.75-2.43) from unintentional injury, and 2.02 (95% CI, 1.54-2.65) from intentional injury. Conclusions and Relevance: In this population-based cohort study of 2.2 million female teenagers, teen pregnancy was associated with future premature mortality. It should be assessed whether supports for female teenagers who experience a pregnancy can enhance the prevention of subsequent premature mortality in young and middle adulthood.

Voices from the Ground: Community Perspectives on Preventing Unintentional Child Injuries in Low-Income Settings.

Unintentional injuries significantly contribute to mortality and morbidity among children under five, with higher prevalence in low- and middle-income countries (LMICs). Deprived communities in these regions face increased injury risks, yet there is limited research on child safety tailored to their unique challenges. To address this gap, we conducted focus group discussions in rural Uganda, involving parents, village health workers, community leaders, teachers, and maids. The objective was to understand community perceptions around child safety and determine what culturally and age-appropriate solutions may work to prevent child injuries. Analysis of discussions from ten focus groups revealed five main themes: injury causes, child development and behavior, adult behavior, environmental factors, and potential safety kit components. Common injuries included falls, burns, drowning, and poisoning, often linked to environmental hazards such as unsafe bunk beds and wet floors. Financial constraints and limited space emerged as cross-cutting issues. Participants suggested educational resources, first aid knowledge, and practical devices like solar lamps as potential solutions. The study presents invaluable insights into child safety in rural Ugandan homes, emphasizing the role of community awareness and engagement in designing effective, accessible interventions. It underscores the importance of context-specific strategies to prevent childhood injuries in similar resource-constrained environments.

Accidental Gunshot Wound Fatalities Related to Dropped Firearms.

ABSTRACT: A death resulting from the accidental discharge of a firearm represents a rare but oftentimes preventable tragedy. Such deaths may occur in a variety of settings. One such setting involves the discharge of a loaded firearm, which occurs when it is accidentally dropped, thrown, or falls to the ground. We report on 3 cases in which a loaded firearm discharged when it was dropped, resulting in the deaths of 3 individuals. In 2 cases, the person carrying the dropped firearm was killed, whereas in the third case, a child standing near the person who dropped the weapon was killed. We discuss the risk factors involved in these tragic incidents and present preventive strategies.

A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization.

Skin cancer is a frequently occurring and possibly deadly disease that necessitates prompt and precise diagnosis in order to ensure efficacious treatment. This paper introduces an innovative approach for accurately identifying skin cancer by utilizing Convolution Neural Network architecture and optimizing hyperparameters. The proposed approach aims to increase the precision and efficacy of skin cancer recognition and consequently enhance patients' experiences. This investigation aims to tackle various significant challenges in skin cancer recognition, encompassing feature extraction, model architecture design, and optimizing hyperparameters. The proposed model utilizes advanced deep-learning methodologies to extract complex features and patterns from skin cancer images. We enhance the learning procedure of deep learning by integrating Standard U-Net and Improved MobileNet-V3 with optimization techniques, allowing the model to differentiate malignant and benign skin cancers. Also substituted the crossed-entropy loss function of the Mobilenet-v3 mathematical framework with a bias loss function to enhance the accuracy. The model's squeeze and excitation component was replaced with the practical channel attention component to achieve parameter reduction. Integrating cross-layer connections among Mobile modules has been proposed to leverage synthetic features effectively. The dilated convolutions were incorporated into the model to enhance the receptive field. The optimization of hyperparameters is of utmost importance in improving the efficiency of deep learning models. To fine-tune the model's hyperparameter, we employ sophisticated optimization methods such as the Bayesian optimization method using pre-trained CNN architecture MobileNet-V3. The proposed model is compared with existing models, i.e., MobileNet, VGG-16, MobileNet-V2, Resnet-152v2 and VGG-19 on the "HAM-10000 Melanoma Skin Cancer dataset". The empirical findings illustrate that the proposed optimized hybrid MobileNet-V3 model outperforms existing skin cancer detection and segmentation techniques based on high precision of 97.84%, sensitivity of 96.35%, accuracy of 98.86% and specificity of 97.32%. The enhanced performance of this research resulted in timelier and more precise diagnoses, potentially contributing to life-saving outcomes and mitigating healthcare expenditures.

Validity and reliability International Classification of Diseases-10 codes for all forms of injury: A systematic review.

BACKGROUND: Intentional and unintentional injuries are a leading cause of death and disability globally. International Classification of Diseases (ICD), Tenth Revision (ICD-10) codes are used to classify injuries in administrative health data and are widely used for health care planning and delivery, research, and policy. However, a systematic review of their overall validity and reliability has not yet been done. OBJECTIVE: To conduct a systematic review of the validity and reliability of external cause injury ICD-10 codes. METHODS: MEDLINE, EMBASE, COCHRANE, and SCOPUS were searched (inception to April 2023) for validity and/or reliability studies of ICD-10 external cause injury codes in all countries for all ages. We examined all available data for external cause injuries and injuries related to specific body regions. Validity was defined by sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Reliability was defined by inter-rater reliability (IRR), measured by Krippendorff's alpha, Cohen's Kappa, and/or Fleiss' kappa. RESULTS: Twenty-seven published studies from 2006 to 2023 were included. Across all injuries, the mean outcome values and ranges were sensitivity: 61.6% (35.5%-96.0%), specificity: 91.6% (85.8%-100%), PPV: 74.9% (58.6%-96.5%), NPV: 80.2% (44.6%-94.4%), Cohen's kappa: 0.672 (0.480-0.928), Krippendorff's alpha: 0.453, and Fleiss' kappa: 0.630. Poisoning and hand and wrist injuries had higher mean sensitivity (84.4% and 96.0%, respectively), while self-harm and spinal cord injuries were lower (35.5% and 36.4%, respectively). Transport and pedestrian injuries and hand and wrist injuries had high PPVs (96.5% and 92.0%, respectively). Specificity and NPV were generally high, except for abuse (NPV 44.6%). CONCLUSIONS AND SIGNIFICANCE: The validity and reliability of ICD-10 external cause injury codes vary based on the injury types coded and the outcomes examined, and overall, they only perform moderately well. Future work, potentially utilizing artificial intelligence, may improve the validity and reliability of ICD codes used to document injuries.