Validation Study Research Articles

Identifying COVID-19 confirmed patients at elevated risk for mortality and mechanical ventilation using a novel criteria for hyperinflammatory syndrome: A retrospective cohort, single-center, validation study

Identifying COVID-19 confirmed patients at elevated risk for mortality and mechanical ventilation using a novel criteria for hyperinflammatory syndrome: A retrospective cohort, single-center, validation study

Traumatic injury to the posterior fossa: a secondary analysis and description of case series from the NEXUS head injury dataset

Traumatic brain injuries involving the posterior fossa are rare and case reports indicate they often result in severe outcomes. We seek to describe characteristics and outcomes of traumatic posterior fossa injuries. We performed a planned secondary analysis of all patients with posterior fossa injuries enrolled in the NEXUS head computed tomography (CT) validation study dataset. The dataset includes prospectively collected data on all patients undergoing non-contrast cranial CT following blunt traumatic head injury from April 2006 to December 2015, at four emergency departments comprising community and university sites, as well as urban, suburban and rural settings in California (Antelope Valley Hospital, San Francisco General Hospital, UCLA Ronald Reagan Medical Center, UCSF Fresno Community Regional Medical Center). We classified each patient into one of three injury patterns: Type I-notable traumatic injuries primarily above the tentorium, with minimal posterior fossa involvement; Type II-notable traumatic injuries both above and within the posterior fossa; and Type III-notable traumatic injuries primarily within the posterior fossa. We extracted demographic data for each patient as well as physician assessments of the NEXUS head CT and Canadian Head CT rule clinical criteria, mechanisms of injury, patient outcomes, and the location and types of intracranial injuries sustained. Of 11,770 patients in the database, 184 (1.6%) had posterior fossa injuries on CT imaging. Mean age was 55.4 years (standard deviation 22.5 years, range 2-96 years); 131 (71.2%) were males. We identified 63 patients with Type I injuries, 87 with Type II injuries, and 34 Type III injuries. The most common mechanisms of injury were falls (41%), pedestrian vs automobile (15%), and motor vehicle collisions (13%). On presentation most patients had altered mental status (72%), abnormal behavior (53%), or a neurologic deficit (55%). The majority of individuals, 151 (82%), had clinically important injuries and 111 (60%) required neurosurgical intervention. The dispositions for the subjects included 52 deaths (28%), 49 (27%) patients discharged home, and 48 (26%) discharged to rehabilitation facilities. When compared to individuals with Type I and Type II injuries, patients with Type III injuries had lower mortality (6% vs 30% and 35%) and higher percentage of patients discharged home (60% vs 19% and 21%). Patients with Type I and II injury patterns (those that involve both the posterior fossa and supratentorium) experienced high mortality and disability. Patients with Type III injuries (isolated posterior fossa) had a better prognosis. None.

Systemic low-grade C-reactive protein is associated with proximal symptom spread in carpal tunnel syndrome.

Neuropathic pain is a highly prevalent condition associated with persistent disability. Some patients with neuropathic pain experience symptom spread outside neuroanatomical boundaries; these patients report more severe sensory symptoms and greater disability. However, the mechanisms behind such symptom spread are not fully understood. We used pre-surgical carpal tunnel syndrome (CTS) as a human model system of neuropathic pain to identify differences in the concentration of serologic inflammatory mediators between patients with CTS with territorial symptoms and those with proximal symptom spread to either the elbow or shoulder/neck. We performed a post-hoc analysis, comparing levels of serologic inflammatory mediators in a discovery cohort among 3 symptoms spread profiles (n = 55; n = 25 no spread, n = 21 spread to elbow, n = 9 spread to shoulder/neck). We then de-novo analysed the significantly dysregulated mediators in an independent validation cohort (n = 72; n = 34 no spread, n = 16 spread to elbow, n = 22 spread to shoulder/neck). The discovery cohort revealed higher serum concentrations of C-reactive protein (CRP) and interleukin-6 in patients with any symptom spread proximal to the wrist; interferon-γ was higher in patients with symptom spread to the elbow compared with those without proximal spread. The validation study replicated the association of higher CRP concentrations in patients with proximal spread to the elbow (no spread: median [interquartile range] 2.5 [5.4]; spread to elbow 6.2 [4.6]; spread to shoulder/neck 2.6 [3.7], P = 0.006). No other markers replicated in the validation cohort. Our findings suggest that proximal symptom spread in the context of neuropathic symptoms is associated with low-grade inflammation.

Development and validation of an early diagnosis model for bone metastasis in non-small cell lung cancer based on serological characteristics of the bone metastasis mechanism

Bone metastasis significantly impact the prognosis of non-small cell lung cancer (NSCLC) patients, reducing their quality of life and shortening their survival. Currently, there are no effective tools for the diagnosis and risk assessment of early bone metastasis in NSCLC patients. This study employed machine learning to analyze serum indicators that are closely associated with bone metastasis, aiming to construct a model for the timely detection and prognostic evaluation of bone metastasis in NSCLC patients. The derivation cohort consisted of 664 individuals with stage IV NSCLC, diagnosed between 2015 and 2018. The variables considered in this study included age, sex, and 18 specific serum indicators that have been linked to the occurrence of bone metastasis in NSCLC. Variable selection used multivariate logistic regression analysis and Lasso regression analysis. Six machine learning methods were utilized to develop a bone metastasis diagnostic model, assessed with Area Under the Curve (AUC), Decision Curve Analysis (DCA), sensitivity, specificity, and validation cohorts. External validation used 113 NSCLC patients from the Medical Alliance (2019-2020). Furthermore, a prospective validation study was conducted on a cohort of 316 patients (2019-2020) who were devoid of bone metastasis, and followed-up for at least two years to assess the predictive capabilities of this model. The model's prognostic value was evaluated using Kaplan-Meier survival curves. Through variable selection, 11 serum indictors were identified as independent predictive factors for NSCLC bone metastasis. Six machine learning models were developed using age, sex, and these serum indicators. A random forest (RF) model demonstrated strong performance during the training and internal validation cohorts, achieving an AUC of 0.98 (95% CI 0.95-0.99) for internal validation. External validation further confirmed the RF model's effectiveness, yielding an AUC of 0.97 (95% CI 0.94-0.99). The calibration curves demonstrated a high level of concordance between the anticipated risk and the observed risk of the RF model. Prospective validation revealed that the RF model could predict the occurrence of bone metastasis approximately 10.27±3.58 months in advance, according to the results of the SPECT. An online computing platform (https://bonemetastasis.shinyapps.io/shiny_cls_1model/) for this RF model is publicly available and free-to-use by doctors and patients. This study innovatively employs age, gender, and 11 serological markers closely related to the mechanism of bone metastasis to construct an RF model, providing a reliable tool for the early screening and prognostic assessment of bone metastasis in NSCLC patients. However, as an exploratory study, the findings require further validation through large-scale, multicenter prospective studies. This work is supported by the National Natural Science Foundation of China (NO.81974315); Shanghai Municipal Science and Technology Commission Medical Innovation Research Project (NO.20Y11903300); Shanghai Municipal Health Commission Health Industry Clinical Research Youth Program (NO.20204Y034).

The model for assessing disaster literacy in nurses: Instrument development and cross-sectional validation study

The model for assessing disaster literacy in nurses: Instrument development and cross-sectional validation study

Novel AI-based tool for primary tooth segmentation on CBCT using convolutional neural networks: A validation study.

Primary teeth segmentation on cone beam computed tomography (CBCT) scans is essential for paediatric treatment planning. Conventional methods, however, are time-consuming and necessitate advanced expertise. The aim of this study was to validate an artificial intelligence (AI) cloud-based platform for automated segmentation (AS) of primary teeth on CBCT. Its accuracy, time efficiency, and consistency were compared with manual segmentation (MS). A dataset comprising 402 primary teeth (37 CBCT scans) was retrospectively retrieved from two CBCT devices. Primary teeth were manually segmented using a cloud-based platform representing the ground truth, whereas AS was performed on the same platform. To assess the AI tool's performance, voxel- and surface-based metrics were employed to compare MS and AS methods. Additionally, segmentation time was recorded for each method, and intra-class correlation coefficient (ICC) assessed consistency between them. AS revealed high performance in segmenting primary teeth with high accuracy (98 ± 1%) and dice similarity coefficient (DSC; 95 ± 2%). Moreover, it was 35 times faster than the manual approach with an average time of 24 s. Both MS and AS demonstrated excellent consistency (ICC = 0.99 and 1, respectively). The platform demonstrated expert-level accuracy, and time-efficient and consistent segmentation of primary teeth on CBCT scans, serving treatment planning in children.

Testing the accuracy of a four serum microRNA panel for the detection of primary bladder cancer: a discovery and validation study

: Background Bladder cancer (BC) is one of the ten most common cancers worldwide with late detection and early age of diagnosis. There is abundant evidence that early detection and timely intervention can lead to a better prognosis of BC. Substantial evidence has indicated that microRNAs (miRNAs) are specific to different tumor types and are remarkably stable, indicating that serum miRNAs may serve as potential cancer diagnostic markers. This study aimed to identify suitable serum miRNAs to create a panel that can be used to diagnose primary BC. Methods In this study, 18 miRNAs that were differentially expressed in BC were obtained from the PubMed or Gene Expression Omnibus database. Then, 18 BC-related-miRNAs were verified in screening and validation sets created using 56 (28 primary BC vs. 28 NCs) and 168 (84 primary BC vs. 84 NCs) serum samples, respectively. Quantitative reverse transcription-PCR (qRT-PCR) was performed to verify the identity of the differential miRNAs. A multi-miRNA panel with superior diagnostic performance was constructed. TCGA and KEGG databases were used to conduct the survival analysis and bioinformatics analysis, respectively. Results Six serum miRNAs (miR-221-5p, miR-181a-5p, miR-98-5p, miR-15a-5p, miR-222-3p, and miR-197-3p) were significantly aberrantly expressed in the BC patients, while four miRNAs from among them (miR-221-5p, miR-181a-5p, miR-15a-5p, miR-222-3p) were assembled into a panel that showed high diagnostic value (AUC = 0.875, 95% CI: 0.815 - 0.921; sensitivity: 82.14%; and specificity: 85.71%) based on the logistic regression analysis. The survival analysis showed that miR-181a-5p was closely associated with BC prognosis (Log-rank p-value < 0.05). Conclusion The combination of the four miRNAs (miR-221-5p, miR-181a-5p, miR-15a-5p and miR-222-3p) may be a novel non-invasive serological biomarker for BC screening.

Determination of benzophenone derivatives in wastewater by GC–MS/MS combined with in‐port derivatization

AbstractThe analysis of UV filters (UVFs) in water has become increasingly important due to their adverse effects on aquatic organisms and humans. This study describes a method for the determination of benzophenone derivatives UVF in wastewater samples. The selected UVFs are 2‐hydroxy‐4‐methoxybenzophenone (BP‐3), 2,4‐dihydroxybenzophenone (BP‐1), 4‐hydroxybenzophenone (4HB), 2,2′‐dihydroxy‐4‐methoxybenzophenone (DHMB) and lastly, 4,4′‐dihydroxybenzophenone (4DHB). The method includes solid‐phase extraction (SPE) of analytes from wastewater followed by on‐line derivatization with bis(trimethylsilyl)trifluoroacetamide (BSTFA) and analysis with GC‐MS/MS. Method validation studies resulted in good recoveries (86–112%), relative standard deviation RSD = 0.8 and 7.3%, the limits of detection LODs = 1.00–10.8 ng/L, and the limits of quantification LOQs = 3.00–32.3 ng/L. The method was successfully applied to domestic wastewater samples collected from influent and effluent of touristic hotels’ biological wastewater treatment plants. BP‐3 (24–1765 ng/L), BP‐1 (8–703 ng/L), 4HB (26–96 ng/L), and 4DHB (20–22 ng/L) were the common benzophenone derivatives in the influent wastewater while effluent contained mainly BP‐1 (8–32 ng/L), 4HB (12–57 ng/L) and 4DHB (20–102 ng/L). These results indicate that BP‐3 and BP‐1 are biodegraded in the treatment processes. However, 4HB and 4DHB are resistant to degradation and they are the main benzophenone metabolites discharged to receiving media.

Gauge invariance, gauge field quantization, and renormalization in autoregularization

Autoregularization, a new divergence-free framework for calculating scattering amplitudes, uses a Lorentz-invariant scale harvested from the kinematics of a scattering process to regularize the amplitude of the process [N. Prabhu, .]. Preliminary validation studies show that autoregularization’s predictions are in good agreement with experimental data—across several scattering processes and a wide range of energy scales. Further, tree-level calculation of the vacuum energy density of the free fields in the Standard Model, using autoregularization, is shown to yield a value that is smaller than the current estimate of the cosmic critical density. In this paper, we prove that the scattering amplitudes in QED, calculated using autoregularization, are gauge invariant. Our proof, which is valid both for autoregularization and current theory, is stronger in that it shows the amplitude of every Feynman diagram is gauge invariant in contrast to previous proofs, which establish gauge invariance only for sum of amplitudes of Feynman diagrams of a process. Next, we show that—unlike in the standard quantization framework, which requires modification of both the quantization framework itself as well as the Lagrangian in order to quantize gauge fields in covariant gauge—in autoregularization the gauge field in QED can be quantized, in covariant gauge, without modifying the standard quantization procedure or the Lagrangian and without introducing the ghost field. Finally, we illustrate renormalization based on autoregularization up to 1-loop in φ4 theory. Since perturbative corrections are finite in autoregularization, the counterterms are not designed to remove divergences but to implement renormalization prescriptions at every order of perturbation. We also derive the renormalization group equation (RGE). Unlike in some regularization schemes (such as dimensional regularization), in which the physical meaning of the fictitious scale introduced by regularization is unclear, in autoregularization the scale in RGE has a transparent physical meaning—it is the Lorentz-invariant kinematic scale of the scattering process of interest. The increasing simplifications resulting within autoregularization and the agreement between its predictions and experimental data, together with the underlying thermodynamic argument, which shows that the framework is essential for a complete description of quantum fields, all converge to suggest that autoregularization provides the proper framework for the description of quantum fields. Published by the American Physical Society 2024

Digital literacy scale: Validity and reliability study with the rasch model

AbstractThe study aimed to develop a Likert-type measurement tool (Digital Literacy Scale, DLS) to determine the digital literacy levels of secondary school students. The validity and reliability of the developed measurement tool were verified using the Rasch model. The Rasch Model can estimate missing data and allow for small study groups (Rasch, Rasch, Probabilistic models for some intelligence and attainment tests, Danish Institute for Educational Research, 1960). Additionally, this model can verify the expected pattern of the measurement tool by calibrating it across participants and items. The content validity of DLS was ensured by expert opinion, and the construct validity was ensured by using the Rach model. In the content validity study, a 25-item pool was created for the draft DLS, and 5 items were removed from the draft DLS at this stage. Construct validity studies were carried out with the remaining 20 items using the classical test theory and item response theory (Rasch model). Exploratory Factor Analysis (EFA) and first-level Confirmatory Factor Analysis (CFA) were used within the scope of classical test theory. Then some Rasch assumptions such as dimensionlessness, local independence, monotonicity, and bias were tested for DLS. In the validity and reliability analysis of DLS, no items were eliminated, and the quantitative theoretical results were statistically confirmed. The analysis results showed that the minimum statistical values required for a good measurement tool were met. Accordingly, 20 valid and reliable compatible items that can be used to determine the digital literacy status of secondary school students were produced.

A novel support vector machine-based 1-day, single-dose prediction model of genotoxic hepatocarcinogenicity in rats.

The development of a rapid and accurate model for determining the genotoxicity and carcinogenicity of chemicals is crucial for effective cancer risk assessment. This study aims to develop a 1-day, single-dose model for identifying genotoxic hepatocarcinogens (GHCs) in rats. Microarray gene expression data from the livers of rats administered a single dose of 58 compounds, including 5 GHCs, was obtained from the Open TG-GATEs database and used for the identification of marker genes and the construction of a predictive classifier to identify GHCs in rats. We identified 10 gene markers commonly responsive to all 5 GHCs and used them to construct a support vector machine-based predictive classifier. In the silico validation using the expression data of the Open TG-GATEs database indicates that this classifier distinguishes GHCs from other compounds with high accuracy. To further assess the model's effectiveness and reliability, we conducted multi-institutional 1-day single oral administration studies on rats. These studies examined 64 compounds, including 23 GHCs, with gene expression data of the marker genes obtained via quantitative PCR 24h after a single oral administration. Our results demonstrate that qPCR analysis is an effective alternative to microarray analysis. The GHC predictive model showed high accuracy and reliability, achieving a sensitivity of 91% (21/23) and a specificity of 93% (38/41) across multiple validation studies in three institutions. In conclusion, the present 1-day single oral administration model proves to be a reliable and highly sensitive tool for identifying GHCs and is anticipated to be a valuable tool in identifying and screening potential GHCs.

Validation of the Perceived Community Support Questionnaire (PCSQ) for Older Chinese.

Community support has a profound positive impact on older people's health and plays a crucial role in facilitating aging in place. This impact is particularly significant in the Chinese context, where community support can alleviate the pressure on traditional family caregiving. This study translated, adapted, and validated the perceived community support questionnaire (PCSQ) for use with older Chinese. Data for this validation study were collected from a cross-sectional survey of 1,064 Chinese aged 65 years and above. Following the cultural adaptation of the PCSQ-14, confirmatory factor analysis (CFA) was conducted to examine its factor structure. Criterion validity, convergent and discriminant validity, internal consistency reliability, and test-retest reliability of the scale were also assessed. Based on the CFA results, the adapted PCSQ-14 was found to have a three-factor solution for community integration, community participation, and community organizations. The criterion-related validity was supported by its significant correlation with depression. Convergent and discriminant validity were established, with the internal consistency of the scale being 0.90. The test-retest reliability intraclass correlation coefficient for the scale was 0.78. The adapted version of the PCSQ-14 demonstrated promising psychometric properties in assessing perceived community support in older Chinese. The adapted PCSQ-14 could assist researchers in determining older Chinese adults' perceptions of community support, and could also assist practitioners and policymakers in developing targeted interventions for older people and allocating resources more effectively.

Temporal relationship between hepatic steatosis and blood pressure elevation and the mediation effect in the development of cardiovascular disease.

The temporal relationship between non-alcoholic fatty liver disease (NAFLD) and hypertension remains highly controversial, with ongoing debates on whether NAFLD induces hypertension or vice versa. We employed cross-lagged panel models to investigate the temporal relationship between hepatic steatosis (assessed by Fatty Liver Index [FLI] in the main analysis, and by Proton Density Fat Fraction [PDFF] in the validation study) and blood pressure (systolic and diastolic blood pressure [SBP/ DBP]). Subsequently, we employed causal mediation models to explore the mediation effect in CVD development, including ischemic heart disease and stroke. The main analysis incorporated repeated measurement data of 5,047 participants from the China Multi-Ethnic Cohort (CMEC) and 5,685 participants from the UK Biobank (UKB). In both cohorts, the path coefficients from FLI to blood pressure were significant and greater than the path from blood pressure to FLI, with βFLI→SBP = 0.081, P < 0.001 versus βSBP→FLI = 0.020, P = 0.031; βFLI→DBP = 0.082, P < 0.001 versus βDBP→FLI = -0.006, P = 0.480 for CEMC, and βFLI→SBP = 0.057, P < 0.001 versus βSBP→FLI = -0.001, P = 0.727; βFLI→DBP = 0.061, P < 0.001, versus βDBP→FLI = -0.006, P = 0.263 for UKB. The validation study with 962 UKB participants using PDFF consistently supported these findings. In the mediation analyses encompassing 11,108 UKB participants, SBP and DBP mediated 12.2% and 5.2% of the hepatic steatosis-CVD association, respectively. The proportions were lower for ischemic heart disease (SBP: 6.1%, DBP: non-statistically significant -6.8%), and relatively stronger for stroke (SBP: 19.4%, DBP: 26.1%). In conclusion, hepatic steatosis more strongly contributes to elevated blood pressure than vice versa. Blood pressure elevation positively mediates the hepatic steatosis-CVD association, particularly in stroke compared to ischemic heart disease.

Validation of the Mediated Learning Observation Instrument Among Children With and Without Developmental Language Disorder in Dynamic Assessment.

In this validation study, we examined the factor structure of the mediated learning observation (MLO) used during the teaching phase of dynamic assessment. As an indicator of validity, we evaluated whether the MLO factor structure was consistent across children with and without developmental language disorder (DLD). Two hundred twenty-four children (188 typically developing and 36 DLD) from kindergarten to second grade completed a 30-min individual mediated learning session on narrative production. Performance during the session was rated using the 12-item MLO by clinicians on affect, behavior, arousal, and elaboration. Exploratory and confirmatory factor analyses were conducted to establish the factor structure and reliability of the MLO. Factor analysis of the MLO suggested a stable three-factor model with adequate fit indices across kindergarten and school-age samples, across both typically developing and DLD subgroups with good to excellent reliability. The final 11-item MLO (one item was removed due to low factor loading) comprises three subscales including (a) cognitive factor, (b) learning anticipation, and (c) learning engagement. The MLO is a valid and reliable instrument for assessing language learning skills in children with and without DLD during dynamic assessment. Practical implications and suggestions for future research addressing the utilization of MLO in dynamic assessment are provided.

Validation of algorithms in studies based on routinely collected health data: general principles.

Clinicians, researchers, regulators, and other decision-makers increasingly rely on evidence from real-world data (RWD), including data routinely accumulating in health and administrative databases. RWD studies often rely on algorithms to operationalize variable definitions. An algorithm is a combination of codes or concepts used to identify persons with a specific health condition or characteristic. Establishing the validity of algorithms is a prerequisite for generating valid study findings that can ultimately inform evidence-based health care. This paper aims to systematize terminology, methods, and practical considerations relevant to the conduct of validation studies of RWD-based algorithms. We discuss measures of algorithm accuracy; gold/reference standard; study size; prioritizing accuracy measures; algorithm portability; and implication for interpretation. Information bias is common in epidemiologic studies, underscoring the importance of transparency in decisions regarding choice and prioritizing measures of algorithm validity. The validity of an algorithm should be judged in the context of a data source, and one size does not fit all. Prioritizing validity measures within a given data source depends on the role of a given variable in the analysis (eligibility criterion, exposure, outcome or covariate). Validation work should be part of routine maintenance of RWD sources.

Machine Learning-Based Prediction of Suicidal Thinking in Adolescents by Derivation and Validation in 3 Independent Worldwide Cohorts: Algorithm Development and Validation Study.

Suicide is the second-leading cause of death among adolescents and is associated with clusters of suicides. Despite numerous studies on this preventable cause of death, the focus has primarily been on single nations and traditional statistical methods. This study aims to develop a predictive model for adolescent suicidal thinking using multinational data sets and machine learning (ML). We used data from the Korea Youth Risk Behavior Web-based Survey with 566,875 adolescents aged between 13 and 18 years and conducted external validation using the Youth Risk Behavior Survey with 103,874 adolescents and Norway's University National General Survey with 19,574 adolescents. Several tree-based ML models were developed, and feature importance and Shapley additive explanations values were analyzed to identify risk factors for adolescent suicidal thinking. When trained on the Korea Youth Risk Behavior Web-based Survey data from South Korea with a 95% CI, the XGBoost model reported an area under the receiver operating characteristic (AUROC) curve of 90.06% (95% CI 89.97-90.16), displaying superior performance compared to other models. For external validation using the Youth Risk Behavior Survey data from the United States and the University National General Survey from Norway, the XGBoost model achieved AUROCs of 83.09% and 81.27%, respectively. Across all data sets, XGBoost consistently outperformed the other models with the highest AUROC score, and was selected as the optimal model. In terms of predictors of suicidal thinking, feelings of sadness and despair were the most influential, accounting for 57.4% of the impact, followed by stress status at 19.8%. This was followed by age (5.7%), household income (4%), academic achievement (3.4%), sex (2.1%), and others, which contributed less than 2% each. This study used ML by integrating diverse data sets from 3 countries to address adolescent suicide. The findings highlight the important role of emotional health indicators in predicting suicidal thinking among adolescents. Specifically, sadness and despair were identified as the most significant predictors, followed by stressful conditions and age. These findings emphasize the critical need for early diagnosis and prevention of mental health issues during adolescence.

Central Auditory Processing Disorder Screening Inventory LIFE-R: Turkish Adaptation, Validity and Reliability Study

Amaç: Bu çalışmanın amacı, orijinal adı LIFE-R (Listening Inventory For Education-Revised) olan envanterin Türkçe çeviri, adaptasyon ve geçerlilik-güvenirlik çalışmasını yaparak, ülkemize (Santral) İşitsel İşlemleme Bozukluğu’nun taramasında kullanılabilecek bir değerlendirme materyalini kazandırmaktır. Gereç ve Yöntem: Çalışmaya 9-18 yaşları arasında, bilinen/tanısı konmuş işitme, konuşma ya da öğrenme bozukluğu olmayan 115 ilköğretim ve lise öğrencisi ve öğretmenleri dahil edilmiştir. Envanterin dil geçerliliği çalışması yapılmış, gerekli düzenlemeler yapıldıktan sonra dilsel eşdeğerliliğe sahip olduğu saptanarak güvenirlik ve geçerlilik analizleri tamamlanmıştır. Bulgular: Envanterin güvenilirliğinin belirlenmesi amacıyla ölçek ve alt boyutları için Cronbach-alfa iç tutarlılık katsayıları hesaplanmıştır. Bu değerlerin yüksek çıkması, ölçeğin güvenilir bir ölçme aracı olduğunu göstermiştir. Yapı geçerliliğine ilişkin kanıtların elde edilmesi sonucunda özgün formun beş faktörlü yapısına uygun olduğu ve faktör analizi sonucunda yüksek yük değerlerine sahip olunduğu görülmüştür. Ölçeklerdeki maddelerin anlamlarındaki tutarlılığa bakıldığında orijinal ve Türkçe formundan elde edilen maddelerin toplam puanları arasında (p&lt;0.01) pozitif yönde anlamlı bir ilişki bulunmuştur ve ölçeğin orijinal kültürdeki anlamları ile eşdeğerlik sağlanmıştır. Sonuçlar: Türkçe LIFE-R (TLIFE-R), özellikle okul çağındaki çocukların SİİB taramasında geçerli ve güvenilir bir ölçek olarak kullanılabilir. SİİB tarama programları, risk altındaki çocukların erken tespiti ve gerekli önleyici ve eğitici/rehabilite edici yaklaşımların başlatılması açısından son derece önemlidir.

Development of a distance education attitude scale for lifelong learning (DEAS-LLL): a validity and reliability study

ABSTRACT In recent times, the utilization of distance education (DE) has witnessed a notable surge across various sectors, particularly amidst the backdrop of pandemics. This study aimed to develop a comprehensive Distance Education Attitude Scale tailored for Lifelong Learning (LLL) contexts. Methodologically, the research enlisted the participation of 260 physicians, employing a five-point Likert scale design. The content validity index yielded an impressive score of 0.92. Following a rigorous 48-item pilot application among 30 physicians, a refined 37-item draft scale emerged. Through explanatory factor analysis, the scale revealed a robust 4-factor structure comprising 22 items. Model fit indices, gauged through confirmatory factor analysis, underscored the scale's coherence and alignment. In the test-retest phase, an analysis utilizing Pearson Correlation Coefficient yielded a robust correlation coefficient of r = 0.891, signifying high stability over time. Moreover, a paired-sample t-test revealed non-significant differences (t = −0.041, p:0.968), further affirming the scale's reliability. With a commendable Cronbach's alpha value of 0.946, the finalized “Distance Education Attitude Scale in Lifelong Learning” stands as a validated and highly reliable instrument, particularly among physicians. Its applicability extends to academic inquiries regarding distance education and holds promise for validation across diverse adult learning cohorts.

It's not all about residual confounding: a plea for QBA for epidemiologic researchers and educators.

We spend a great deal of time on confounding in our teaching, in our methods development and in our assessment of study results. This may give the impression that uncontrolled confounding is the biggest problem that observational epidemiology faces, when in fact, other sources of bias such as selection bias, measurement error, missing data, and misalignment of zero time may often (especially if they are all present in a single study) lead to a stronger deviation from the truth. Compared to the amount of time we spend teaching how to address confounding in a data analysis, we spend relatively little time teaching methods for simulating confounding (and other sources of bias) to learn their impact and develop plans to mitigate or quantify the bias. We review a paper by Desai et al that uses simulation methods to quantify the impact of an unmeasured confounder when it is completely missing or when a proxy of the confounder is measured. We use this article to discuss how we can use simulations of sources of bias to ensure we generate better and more valid study estimates, and we discuss the importance of simulating realistic datasets with plausible bias structures to guide data collection. If an advanced life form exists outside of our current universe and they came to earth with the goal of scouring the published epidemiologic literature to understand what the biggest problem epidemiologists have, they would quickly discover that the limitations section of publications would provide them with all the information they needed. And most likely what they would conclude is that the biggest problem that we face is uncontrolled confounding. It seems to be an obsession of ours.

Comparison of the ScreenFire and Xpert HPV assays for the detection of human papillomavirus and cervical precancer among women living with HIV in Malawi

BackgroundThe World Health Organization recommends human papillomavirus (HPV) testing for primary cervical cancer screening, including among women living with HIV (WLWH). Low-and-middle-income countries account for 85% of the cervical cancer burden globally, yet have limited access to HPV-based screening, largely due to cost. This study aims to compare the performance of a rapid, isothermal amplification HPV assay (ScreenFire) to that of the Xpert HPV assay for the detection of HPV and cervical precancer among WLWH in Malawi.MethodsWe utilized stored self- and provider-collected specimens from a prospective cohort study of WLWH in Malawi from July 2020 to February 2022. Specimens were tested with both Xpert and ScreenFire HPV assays. The overall and within-channel non-hierarchical agreement between ScreenFire and Xpert was determined for both self- and provider-collected specimens. Hierarchical ScreenFire HPV positivity by channel was compared to Xpert for each histological diagnosis—cervical intraepithelial neoplasia grade 2 or worse (CIN2+) compared to <CIN2.Results315 matched self- and provider-collected specimens had valid results from both Xpert and ScreenFire testing and were included in analyses, of which 279 and 36 were HPV positive and HPV negative, respectively, on Xpert self-collection. Of the 315, 245 (78%) had normal pathology, 21 CIN1 (7%), 14 CIN2 (4%), and 35 CIN3 (11%). Of the 245 with normal pathology, 213 (87%) and 188 (77%) were HPV-positive on Xpert and ScreenFire self-collected specimens, respectively. Among provider-collected specimens, the assays had 80% agreement on overall HPV positivity (unweighted kappa 0.59, 95% 0.50–0.69). ScreenFire was HPV-positive in 90% of self-collected specimens that were HPV-positive on Xpert. Channel agreement between the assays was high for both self- and provider-collected specimens, but slightly lower for HPV18/45. In hierarchical analysis, ScreenFire demonstrated high concordance with Xpert testing for detecting CIN2+ cases in all channels, missing no HPV 16 or HPV 18/45 positive CIN2+ case that was positive on Xpert, in both self- and provider-collected specimens.ConclusionIn this study of stored specimens, the ScreenFire HPV assay performed well in the detection of HPV and CIN2+ among WLWH compared to the Xpert HPV assay. If supported by larger validation studies, ScreenFire could be an affordable alternative point-of-care HPV assay for use in LMICs.