Critical Value Research Articles

Optimizing VAT threshold: The key to improving VAT performance

The low tax ratio in Indonesia is an issue often associated with suboptimal tax revenue performance. This study examines the role of the threshold for Value Added Tax (VAT), which is suspected to significantly influence the optimization of VAT revenue, while also exploring the effect of VAT rate and Gross Domestic Product (GDP) per capita. The study analyze data from member countries of the Organization for Economic Co-operation and Development (OECD) in 2022 using multiple linear regression methods. The results show that increases in the VAT threshold and VAT rate positively impact VAT revenue performance, while an increase in per capita GDP has a negative effect. A higher VAT threshold allows the country to focus on larger taxpayers, while the Micro, Small, and Medium Enterprises (MSMEs) benefit from reduced administrative burdens. These findings can serve as a basis for government evaluation in determining the appropriate threshold and VAT rates to enhance tax revenue performance optimally.

Empirical Inferences Under Bayesian Framework to Identify Cellwise Outliers

Outliers are typically identified using frequentist methods. The data are classified as “outliers” or “not outliers” based on a test statistic that measures the magnitude of the difference between a value and the majority part of the data. The threshold for a data value to be an outlier is typically defined by the user. However, a subjective choice of the threshold increases the uncertainty associated with outlier status for each data value. A cellwise outlier detection algorithm named FuzzyHRT is used to automate the editing process in repeated surveys. This algorithm uses Bienaymé–Chebyshev’s inequality and fuzzy logic to detect four different types of outliers resulting from format inconsistencies, historical, tail, and relational anomalies. However, fuzzy logic is not suited for probabilistic reasoning behind the identification of anomalous cells. Bayesian methods are well suited for quantifying the uncertainty associated with the identification of outliers. Although, as suggested by the literature, there exist well-developed Bayesian methods for record-level outlier detection, Bayesian methods for identifying outliers within individual records (i.e., at the cell level) remain unexplored. This paper presents two approaches from the Bayesian perspective to study the uncertainty associated with identifying outliers. A Bayesian bootstrap approach is explored to study the uncertainty associated with the output scores from the FuzzyHRT algorithm. Empirical likelihoods in a Bayesian setting are also considered for probabilistic reasoning behind the identification of anomalous cells. NASS survey data for livestock and major crop yield (such as corn) are considered for comparing the performances of the two proposed approaches with recent cellwise outlier methods.

Deal Value Threshold and M&A: A Competition Law Analysis

In 2023, the Indian Parliament introduced amendments to the Competition Act, 2002. The primary focus of these amendments is to provide regulatory certainty, ensure faster market correction, and provide a trust-based business environment in India. These amendments also aim to give the Competition Commission of India (CCI) adequate power to ensure that no anti-competitive practices are undertaken in India. A plethora of amendments have been enacted, and one of the key amendments impacting M&A is the introduction of the Deal Value Threshold (DVT) while retaining the asset and turnover test. The rationale for this amendment is to ensure digital markets are covered within the ambit of DVT and to ensure that no anti-competitive M&A deals are undertaken in the digital market space. The impact of the new additional threshold in India has been analyzed, and the loopholes in the DVT framework have been highlighted. The impact of DVT on M&A deals undertaken in India has been showcased. In addition, solutions have been provided that can be adopted by the CCI in order to plug the loopholes in this new framework to make it comprehensive.

Evidenced-Based Prior for Estimating the Treatment Effect of Phase III Randomized Trials in Oncology.

The primary results of phase III oncology trials may be challenging to interpret, given that results are generally based on P value thresholds. The probability of whether a treatment is beneficial, although more intuitive, is not usually provided. Here, we developed and released a user-friendly tool that calculates the probability of treatment benefit using trial summary statistics. We curated 415 phase III randomized trials enrolling 338,600 patients published between 2004 and 2020. A phase III prior probability distribution for the treatment effect was developed on the basis of a three-component zero-mean mixture distribution of the observed z-scores. Using this prior, we computed the probability of clinically meaningful benefit (hazard ratio [HR] <0.8). The distribution of signal-to-noise ratios and power of phase III oncology trials were compared with that of 23,551 randomized trials from the Cochrane Database. The signal-to-noise ratios of phase III oncology trials tended to be much larger than randomized trials from the Cochrane Database. Still, the median power of phase III oncology trials was only 49% (IQR, 14%-95%), and the power was <80% in 65% of trials. Using the phase III oncology-specific prior, only 53% of trials claiming superiority (114 of 216) had a ≥90% probability of clinically meaningful benefits. Conversely, the probability that the experimental arm was superior to the control arm (HR <1) exceeded 90% in 17% of trials interpreted as having no benefit (34 of 199). By enabling computation of contextual probabilities for the treatment effect from summary statistics, our robust, highly practical tool, now posted on a user-friendly webpage, can aid the wider oncology community in the interpretation of phase III trials.

Preliminary Evaluation of the Value of a Small-Molecule Probe Targeting DNMT1 in Detecting the Methylation of PAX1 in Cervical Cancer.

To investigate the screening value of a small-molecule probe to assess the methylation of PAX1 in cervical cancer. The diagnostic threshold of the grayscale values for cervical lesions was assessed by plotting the Receiver Operating Characteristic (ROC) curve of subjects. Grayscale values were significantly different among the four groups (p<0.05). Compared with the LSIL and cervicitis groups, a considerably higher grayscale value was found in the CA and high-grade squamous intraepithelial lesion (HSIL) groups (both p<0.05). The differential ROC curves of the grayscale values showed that the diagnostic Area Under Curve of the probe for cervicitis and low-grade squamous intraepithelial lesion (LSIL) was 0.8724 (95% CI=0.7762-0.9685, p<0.0001), for cervicitis and CA was 1.0000 (p<0.0001), for the LSIL and HSIL was 0.5484 (95% CI = 0.3826-0.7142, p=0.5755), and for the LSIL and CA was 0.7724 (95% CI = 0.6016-0.9432, p = 0.0138). The small molecular probe has certain application value in differentiating the type of cervical lesions and has better efficacy in distinguishing cervical inflammatory and precancerous lesions from carcinogenesis, but less efficacy in determining the type of precancerous lesions.

Maternal serum ischemia-modified albumin as an oxidative stress biomarker in preterm pre-labor rupture of membranes.

To evaluate the maternal serum ischemia-modified albumin (IMA) concentration as an oxidative stress biomarker in pregnancies complicated by preterm pre-labor rupture of membranes (PPROM) without maternal clinical infection and compare these results with healthy pregnancies. The present cohort study included 40 pregnancies complicated by PPROM and 49 similar gestational age healthy pregnancies in the third trimester of gestation. Maternal venous blood specimens were obtained at the day of first diagnosis. Maternal serum IMA level was assayed with an Albumin Cobalt Binding test. The subjects were followed up until delivery and perinatal outcomes were recorded. The maternal serum IMA concentrations were significantly higher in the study group (0.56 ± 0.05 absorbance units) as compared to controls (0.54 ± 0.03 absorbance units) (p = 0.020). The maternal serum IMA concentrations were not significantly correlated with the initial maternal white blood cell count (r: 0.118, p = 0.269) and C-reactive protein levels (r: 0.066, p = 0.541). The maternal serum IMA concentrations were negatively correlated with gestational age at delivery (r: -0.248, p = 0.019), birthweight (r: -0.247, p = 0.020) and Apgar scores (r: -0.200, p = 0.049; r: -0.245, p = 0.020). The threshold value of maternal serum IMA concentration above 0.55 absorbance units indicated the pregnancy complicated by PPROM by 57.5% sensitivity and 57.1% specificity (Area under curve 0.613, confidence interval 0.50-0.73). The current study supported for the first time that there is an association between increased maternal serum IMA levels and the development of PPROM in the third trimester of gestation without maternal clinical infection. Elevated maternal serum IMA levels may alert the obstetrician about poor ongoing perinatal outcomes in the early phase of PPROM before increased maternal C-reactive protein and white blood cell count.

Effects of Mixed Metal Exposure on MRI Metrics in Basal Ganglia.

Welding fumes contain various metals. Past studies, however, mainly focused on Manganese (Mn)-related neurotoxicity. This study investigated welding-related mixed metal exposure effects on MRI metrics in the basal ganglia (BG) and their dose-response relationship. Subjects with (N = 23) and without (N = 24) a welding exposure history were examined. Metal exposure was estimated with exposure history questionnaire and whole blood metal levels. T1 (weighted-intensity and relaxation time; estimates of brain Mn accumulation), diffusion tensor imaging [Axial (AD), mean (MD), radial diffusivity (RD), and fractional anisotropy (FA); estimates of microstructural differences] metrics in BG [caudate nucleus, putamen, and globus pallidus (GP)] and voxel-based morphometry (for volume) were examined and related with metal exposure measures. Compared to controls, welders showed higher GP R1 (1/T1; p = 0.034) but no differences in blood metal and T1-weighted (T1W) values in any ROIs (p's > 0.120). They also had higher AD and MD values in the GP (p's < 0.033) but lower FA values in the putamen (p = 0.039) with no morphologic differences. In welders, higher blood Mn and Vanadium (V) levels predicted higher BG R1 and T1W values (p's < 0.015). There also were significant overall metal mixture effects on GP T1W and R1 values. Moreover, GP AD and MD values showed non-linear associations with BG T1W values: They increased with increasing T1W values only above certain threshold of T1 values. The current findings suggest that Mn and V individually but also metal mixtures jointly predict GP T1 signals that may in turn contribute to altered DTI metrics in the BG after certain exposure threshold levels.

Associations of "Weekend Warrior" Physical Activity With Incident Disease and Cardiometabolic Health.

Achievement of guideline-recommended levels of physical activity (≥150 minutes of moderate-to-vigorous physical activity per week) is associated with lower risk of adverse cardiovascular events and represents an important public health priority. Although physical activity commonly follows a "weekend warrior" pattern, in which most moderate-to-vigorous physical activity is concentrated in 1 or 2 days rather than spread more evenly across the week (regular), the effects of physical activity pattern across a range of incident diseases, including cardiometabolic conditions, are unknown. We tested associations between physical activity pattern and incidence of 678 conditions in 89 573 participants (62±8 years of age; 56% women) of the UK Biobank prospective cohort study who wore an accelerometer for 1 week between June 2013 and December 2015. Models were adjusted for multiple baseline clinical factors, and P value thresholds were corrected for multiplicity. When compared to inactive (<150 minutes moderate-to-vigorous physical activity/week), both weekend warrior (267 total associations; 264 [99%] with lower disease risk; hazard ratio [HR] range, 0.35-0.89) and regular activity (209 associations; 205 [98%] with lower disease risk; HR range, 0.41-0.88) were broadly associated with lower risk of incident disease. The strongest associations were observed for cardiometabolic conditions such as incident hypertension (weekend warrior: HR, 0.77 [95% CI, 0.73-0.80]; P=1.2×10-27; regular: HR, 0.72 [95% CI, 0.68-0.77]; P=4.5×10-28), diabetes (weekend warrior: HR, 0.57 [95% CI, 0.51-0.62]; P=3.9×10-32; regular: HR, 0.54 [95% CI, 0.48-0.60]; P=8.7×10-26), obesity (weekend warrior: HR, 0.55 [95% CI, 0.50-0.60]; P=2.4×10-43, regular: HR, 0.44 [95% CI, 0.40-0.50]; P=9.6×10-47), and sleep apnea (weekend warrior: HR, 0.57 [95% CI, 0.48-0.69]; P=1.6×10-9; regular: HR, 0.49 [95% CI, 0.39-0.62]; P=7.4×10-10). When weekend warrior and regular activity were compared directly, there were no conditions for which effects differed significantly. Observations were similar when activity was thresholded at the sample median (≥230.4 minutes of moderate-to-vigorous physical activity/week). Achievement of measured physical activity volumes consistent with guideline recommendations is associated with lower risk for >200 diseases, with prominent effects on cardiometabolic conditions. Associations appear similar whether physical activity follows a weekend warrior pattern or is spread more evenly throughout the week.

Alcohol, smoking, and brain structure: common or substance specific associations.

Alcohol use and smoking are common substance-use behaviors with well-established negative health effects, including decreased brain health. We examined whether alcohol use and smoking were associated with the same neuroimaging-derived brain measures. We further explored whether the effects of alcohol use and smoking on the brain were additive or interactive. We leveraged a cohort of 36,309 participants with neuroimaging data from the UK Biobank. We used linear regression to determine the association between 354 neuroimaging-derived brain measures and alcohol use defined as drinks per week, pack years of smoking, and drinks per week x pack years smoking interaction. To assess whether the brain associations with alcohol are broadly similar or different from the associations with smoking, we calculated the correlation between z-scores of association for drinks per week and pack years smoking. Results indicated overall moderate positive correlation in the associations across measures representing brain structure, magnetic susceptibility, and white matter tract microstructure, indicating greater similarity than difference in the brain measures associated with alcohol use and smoking. The only evidence of an interaction between drinks per week and pack years smoking was seen in measures representing magnetic susceptibility in subcortical structures. The effects of alcohol use and smoking on brain health appeared to be additive rather than multiplicative for all other brain measures studied. 97% (224/230) of associations with alcohol and 100% (167/167) of the associations with smoking that surpassed a p value threshold are in a direction that can be interpreted to reflect reduced brain health. Our results underscore the similarity of the adverse associations between use of these substances and neuroimaging derived brain measures.

Psychiatric evidence in immigration and asylum cases – a tale of two experts

SUMMARY In an article in this issue of BJPsych Advances a courageous psychiatrist describes judicial criticism of his expert testimony in a case before the UK's Upper Tribunal (Immigration and Asylum Chamber). This commentary reflects on the value of criticism and feedback on expert witness work, contrasting the psychiatrist's positive response to the judge's words with the reaction of an expert witness in clinical negligence case, who rejected criticism of his evidence.

Exploring thermal flow dynamics in pressurized water reactors using hybrid graphene nanoplatelet coolants

AbstractThis study investigates the impact of hybrid nanoparticles on the temperature of nuclear reactor coolant, with a focus on graphene nanoplatelet (GNP)‐based hybrid nanoparticles. Sixteen different hybrid nanofluids were analyzed, and their performance was compared with a standard water‐based coolant. The criticality values were obtained through MCNP modeling, revealing that higher nanoparticle ratios led to increased criticality, with the highest value of 1.3239 observed in GNP‐Fe3O4 + Al2O3 nanofluids (0.05 wt%) and the lowest value of 1.2935 in GNP–Fe3O4 + SiO2 nanofluids (0.001 wt%). Temperature variations showed that increasing nanoparticle concentrations resulted in slightly higher temperatures, with a maximum of 611.97 K for 0.05 vol.% GNP nanoparticles. Additionally, the departure from nucleate boiling ratio values were consistently above the safety threshold of 2.08, with the lowest value of 3.657 for GNP–Fe3O4 + SiO2 nanofluids (0.05 vol.%). These findings suggest that hybrid nanofluids, particularly those with higher nanoparticle ratios, can enhance the thermal performance and safety margins of nuclear reactor coolants, offering a promising avenue for future research and application.

Practical non-stationary extreme value analysis of peaks over threshold using the generalised Pareto distribution: Estimating uncertainties in return values

Choice of tuning parameters influences the performance of non-stationary extreme value modelling for peaks over threshold using the generalised Pareto (GP) distribution. We examine the effect of tuning parameter choice on maximum roughness-penalised likelihood estimation of GP models, the shape and scale parameters of which are assumed to vary smoothly on a one-dimensional “directional” covariate domain, under a B-spline representation. We examine the effect of (a) extreme value model parameterisation, (b) relative roughness penalty of GP parameters as a function of covariate, (c) cross-validation strategy for roughness parameter tuning, and (d) estimator for return value, on the estimation of return values corresponding to return periods 1000× the period of a sample of size 1000. Bootstrap resampling is used for thorough uncertainty quantification. We also compare results with those from stationary inference.Results from a large simulation study of 16 cases broadly representative of North Sea conditions for significant wave height with direction, indicate that (i) multiple two-group cross-validation yields lower return value estimates that ten-group cross-validation (leading to negative bias on average, for the case studies considered), (ii) the quantile of the bootstrap predictive estimator yields larger values than the mean over bootstraps of the quantile estimate (leading to reduced omni-directional bias for the case studies considered). Further, (iii) the use of stationary models for non-stationary tails is only reasonable when a high extreme value threshold is set for the stationary analysis. However, (iv) the relative performance of different modelling strategies is sensitive to the specific characteristics of the case study.

Integration of interpretable machine learning and environmental magnetism elucidates reduction mechanism of bioavailable potentially toxic elements in lakes after monsoon

Little information is available on the influence of substantial precipitation and particulate matter entering during the monsoon process on the release of potentially toxic elements (PTEs) into lake sediments. Sediments from a typical subtropical lake across three periods, pre-monsoon, monsoon, and post-monsoon, were collected to determine the chemical forms of 12 PTEs (As, Cd, Co, Cr, Cu, Fe, Hg, Pb, Mn, Ni, Sb, and Zn), magnetic properties, and physicochemical indicators. Feature importance, Shapley additive explanations, and partial dependence plots were used to explore the factors influencing bioavailable PTEs. The proportion of bioavailable forms of PTEs decreased from 3.85 % (Cd) to 87.84 % (Hg) after the monsoon. Gradient extreme boosting demonstrated robust fitting accuracy for the prediction of the bioavailable forms of the 12 PTEs (R2 > 0.84). Shapley additive explanations identified that the bioavailable forms were influenced by the total PTE concentrations, wind, shortwave radiation, and particle inputs (25.1 %–88.5 % for total importance), either individually or in combination. The partial dependence plots highlighted the influence thresholds of background values and anthropogenic factors on the bioavailable forms of PTEs. Changes in environmental properties could indicate the process of external sediment influx into lakes. The optimized model combined with magnetic parameters showed strong performance in other cases (coefficient of determination>0.58), confirming the ubiquitous decrease in bioavailable forms of PTEs in sediments across subtropical lakes after monsoons.

The diagnostic utility of CT attenuation values in detecting calcification within costal cartilage

BackgroundTo establish and verify diagnostic criteria for the identification of costal cartilage calcification based on CT attenuation value. Methods360 chest CT slices of 120 patients were reviewed and annotated retrospectively and ROC curve was used to evaluate the diagnostic ability of CT attenuation value. Another 20 slices containing calcification were randomly selected and annotated by 4 doctors for further validation. H&E and COLX staining was performed on the residual costal cartilage. ResultsIn total 355129 voxels were detected and 187.5 was confirmed as the optimal CT attenuation value threshold, with a sensitivity of 98.6% and a specificity of 99.7%, for costal cartilage calcification diagnosis. Threshold-based identification of calcification demonstrated a similarity of nearly 80% with specialists' assessments, and exhibited advantages in the identification of subtle calcifications in the further validation. We also observed that CT attenuation values among males demonstrated a centralized distribution, whereas those among females exhibited a bimodal distribution. Threshold-based identified calcification showed a positivity of COLX. ConclusionsCT attenuation value could validly and reliably diagnose calcification within costal cartilage. Further investigations involving larger cohorts of patients are required to elucidate the risk factors and underlying mechanisms of costal cartilage calcification.

Nuclear magnetic resonance-based metabolomics with machine learning for predicting progression from prediabetes to diabetes.

Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes. This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites. During a median follow-up of 13.6 years, 2525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine-learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver-operating characteristic curve, 1 year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5 years: 0.830 vs 0.798, 10 years: 0.801 vs 0.776, all p < 0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes. Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions. Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500).

Non-linear response of plant caloric value to N addition and mowing treatments in a meadow steppe

BackgroundCaloric value is an important indicator of grassland ecosystem function, but the response of caloric value to nitrogen (N) addition and mowing is still unclear. We explored the adaptive changes of plant caloric value and energy standing crop along a N addition gradient after six-year NH4NO3 addition and mowing treatments in an Inner Mongolian temperate meadow steppe in northern China.ResultsWe found that the response of plant caloric value to N addition at different organizational levels was diverse. The caloric value of legumes increased linearly with N addition rates. The caloric value of grasses exhibited a non-linear response trend, initially increasing followed by saturation or decrease, with a N response threshold present. Due to the dominance of grass species, the caloric value at the community level followed a similar pattern to that of the grasses along the N addition gradient. Under mowing, the caloric value of plants at each organizational level increased and usually mowing enhanced the N response threshold. Amongst these, the N response threshold of Leymus chinensis increased from 3.302 to 5.443 g N m−2 yr−1, grasses increased from 4.414 to 5.746 g N m−2 yr−1, and community increased from 5.373 to 9.216 g N m−2 yr−1. Under non-mowing treatment, the N response thresholds of the most dominant species, Leymus chinensis, and community energy standing crop were 10.001 and 15.119 g N m−2 yr−1, respectively. Under mowing, the energy standing crops showed a linear increasing trend.ConclusionsN response thresholds of plant caloric value and energy standing crop vary at different organizational levels (community > functional group > species). The results reveal varying regulatory capabilities of plants on the ecological environment at different organizational levels. These findings enhance our understanding of plant-environment interactions in grassland ecosystems under N deposition from an energy perspective, which is of great significance to clarify the response mechanism of grassland ecosystem structure and function to N deposition.

Racial differences in odds of asthma exacerbations among Aspergillus fumigatus–sensitized adults with asthma

BackgroundAllergic sensitization to mold is a risk factor for poor asthma outcomes, but whether it accounts for disparities in asthma outcomes according to race or socioeconomic status is not well-studied. ObjectiveTo identify factors associated with allergic sensitization to molds and evaluate associations of sensitization to molds with asthma exacerbations after stratifying by race. MethodsWe conducted a retrospective cohort study of adults with asthma who had an outpatient visit to a large health system between January 1, 2017 and June 30, 2023 and received aeroallergen testing to Aspergillus fumigatus, Penicillium, Alternaria, and Cladosporium. We used logistic regression models to evaluate factors associated with mold sensitization and the effect of mold sensitization on asthma exacerbations in the 12 months before testing, overall and then stratified by race. ResultsA total of 2732 patients met the inclusion criteria. Sensitization to each mold was negatively associated with being a woman (odds ratios [ORs] ≤ 0.59, P ≤ .001 in 5 models) and positively associated with the Black race (ORs ≥ 2.16 vs White, P < .0005 in 5 models). In the full cohort, sensitization to molds was not associated with asthma exacerbations (ORs = 0.95-1.40, P ≥ .003 in 5 models and all above the corrected P value threshold). Among 1032 Black patients, sensitization to A fumigatus, but not to other molds, was associated with increased odds of asthma exacerbations (OR = 2.04, P < .0005). ConclusionBeing a man and Black race were associated with allergic sensitization to molds. Sensitization to A fumigatus was associated with asthma exacerbations among Black patients but not the overall cohort, suggesting that A fumigatus allergy is a source of disparities in asthma outcomes according to race.

Multiscale Optical Imaging Fusion for Cervical Precancer Diagnosis: Integrating Widefield Colposcopy and High-Resolution Endomicroscopy.

Early detection and treatment of cervical precancers can prevent disease progression. However, in low-resource communities with a high incidence of cervical cancer, high equipment costs and a shortage of specialists hinder preventative strategies. This manuscript presents a low-cost multiscale in vivo optical imaging system coupled with a computer-aided diagnostic system that could enable accurate, real-time diagnosis of high-grade cervical precancers. The system combines portable colposcopy and high-resolution endomicroscopy (HRME) to acquire spatially registered widefield and microscopy videos. A multiscale imaging fusion network (MSFN) was developed to identify cervical intraepithelial neoplasia grade 2 or more severe (CIN 2+). The MSFN automatically identifies and segments the ectocervix and lesions from colposcopy images, extracts nuclear morphology features from HRME videos, and integrates the colposcopy and HRME information. With a threshold value set to achieve sensitivity equal to clinical impression (0.98 [p = 1.0]), the MSFN achieved a significantly higher specificity than clinical impression (0.75 vs. 0.43, p = 0.000006). Our findings show that multiscale optical imaging of the cervix allows the highly sensitive and specific detection of high-grade precancers. The multiscale imaging system and MSFN could facilitate the accurate, real-time diagnosis of cervical precancers in low-resource settings.

Performance of GPT-4 with Vision on Text- and Image-based ACR Diagnostic Radiology In-Training Examination Questions.

Background Recent advancements, including image processing capabilities, present new potential applications of large language models such as ChatGPT (OpenAI), a generative pretrained transformer, in radiology. However, baseline performance of ChatGPT in radiology-related tasks is understudied. Purpose To evaluate the performance of GPT-4 with vision (GPT-4V) on radiology in-training examination questions, including those with images, to gauge the model's baseline knowledge in radiology. Materials and Methods In this prospective study, conducted between September 2023 and March 2024, the September 2023 release of GPT-4V was assessed using 386 retired questions (189 image-based and 197 text-only questions) from the American College of Radiology Diagnostic Radiology In-Training Examinations. Nine question pairs were identified as duplicates; only the first instance of each duplicate was considered in ChatGPT's assessment. A subanalysis assessed the impact of different zero-shot prompts on performance. Statistical analysis included χ2 tests of independence to ascertain whether the performance of GPT-4V varied between question types or subspecialty. The McNemar test was used to evaluate performance differences between the prompts, with Benjamin-Hochberg adjustment of the P values conducted to control the false discovery rate (FDR). A P value threshold of less than.05 denoted statistical significance. Results GPT-4V correctly answered 246 (65.3%) of the 377 unique questions, with significantly higher accuracy on text-only questions (81.5%, 159 of 195) than on image-based questions (47.8%, 87 of 182) (χ2 test, P < .001). Subanalysis revealed differences between prompts on text-based questions, where chain-of-thought prompting outperformed long instruction by 6.1% (McNemar, P = .02; FDR = 0.063), basic prompting by 6.8% (P = .009, FDR = 0.044), and the original prompting style by 8.9% (P = .001, FDR = 0.014). No differences were observed between prompts on image-based questions with P values of .27 to >.99. Conclusion While GPT-4V demonstrated a level of competence in text-based questions, it showed deficits interpreting radiologic images. © RSNA, 2024 See also the editorial by Deng in this issue.

A Hypothesis-Free Approach to Identifying Potential Effects of Relative Age in School Year: an Instrumental Variable Phenome-Wide Association Study in the UK Biobank.

A child's relative age within their school year ('relative age') is associated with educational attainment and mental health. However, hypothesis driven studies often re-examine the same outcomes and exposure, potentially leading to confirmation and reporting biases, and missing unknown effects. Hypothesis-free outcome-wide analyses can potentially overcome these limitations. We conducted a hypothesis-free investigation of the effects of relative age within school year. We used an instrumental variable (IV)-pheWAS in the UK Biobank (participants aged 40-69 years at baseline), using the PHESANT software package. We created two IVs for relative age: being born in September vs. August (n=64 075) and week of birth (n=383 309). Outcomes passing the Bonferroni-corrected P value threshold for either instrument were plotted to identify a discontinuity at the school year transition. 13 traits associated with at least one of the instruments showed a discontinuity. Previously identified effects included those with a younger relative age being less likely to have educational qualifications and more likely to have started smoking at a younger age. We detected a few associations not explored by previous studies. For example, those with younger relative age had better lung function as adults. Hypothesis-free approaches could help address confirmation and reporting biases in epidemiology.