How Both the Chicago School and Ordoliberalism Softened on Big Businesses

In new research, Ryan Stones revisits the alleged disagreement between two influential schools of antitrust on how to handle big businesses. Instead of finding contrasting policy recommendations, he highlights a strikingly similar relaxation of attitudes toward enforcement in the Chicago School and Ordoliberalism in the post-war period

Systematic review and meta-analysis of early visual processing, social cognition, and functional outcomes in schizophrenia spectrum disorders

Non-affective psychotic disorders are marked by cognitive and sensory processing abnormalities, including in early visual processing and social cognition. Understanding the relationships between these deficits and their impact on daily-life functional outcomes may help to improve outcomes in affected individuals. This systematic review and meta-analysis aimed to summarise the existing evidence on the relationships between early visual processing, social cognition, and functional outcomes, and to assess the evidence regarding the mediating role of social cognition in the association between early visual processing and functional outcomes in individuals with schizophrenia spectrum disorders. A comprehensive search across five databases identified 364 potentially eligible studies, with eight articles meeting all inclusion criteria. Meta-analytic techniques were employed to synthesise effect sizes and assess a meta-mediation model. Three random-effects meta-analyses revealed significant associations between all three domains of interest. Social cognition partially mediated the relationship between early visual processing and functional outcomes. The direct effect of early visual processing on functional outcomes remained significant, albeit with a reduced effect size. The findings suggest that interventions targeting both early visual processing and social cognition concurrently may improve functional outcomes more effectively than focusing on either domain alone

Investigating a structured diagnostic approach for chronic breathlessness in primary care: a mixed-methods feasibility cluster randomised controlled trial

Background: There is a need to reduce delays to diagnosis for chronic breathlessness to improve patient outcomes. Objective: To conduct a mixed-methods feasibility study of a larger cluster randomised controlled trial (cRCT) investigating a structured symptom-based diagnostic approach versus usual care for chronic breathlessness in primary care. Methods: 10 general practitioner practices were cluster randomised to a structured diagnostic approach for chronic breathlessness including early parallel investigations (intervention) or usual care. Adults over 40 years old at participating practices were eligible if presenting with chronic breathlessness without an existing explanatory diagnosis. The primary feasibility outcomes were participant recruitment and retention rate at 1 year. Secondary outcomes included number of investigations at 3 months, and investigations, diagnoses and patient-reported outcome measures (PROMs) at 1 year. Semistructured interviews were completed with patients and clinicians, and analysed using thematic analysis. Results: Recruitment rate was 32% (48/150): 65% female, mean (SD) age 66 (11) years, body mass index 31.2 kg/m2 (6.5), median (IQR) Medical Research Council dyspnoea 2 (2–3). Retention rate was 85% (41/48). At 3 months, the intervention group had a median (IQR) of 8 (7–9) investigations compared with 5 (3–6) investigations with usual care. 11/25 (44%) patients in the intervention group had coded diagnosis for breathlessness at 12 months compared with 6/23 (26%) with usual care. Potential improvements in symptom burden and quality of life were observed in the intervention group above usual care. Conclusions: A cRCT investigating a symptom-based diagnostic approach for chronic breathlessness is feasible in primary care showing potential for timely investigations and diagnoses, with PROMs potentially indicating patient-level benefit. A further refined fully powered cRCT with health economic analysis is needed

Increasing myopia in Scotland at age 3.5-5.5 years: a retrospective epidemiological study

Purpose: Scotland has comprehensive child vision screening at age 3.5-5.5 years, with ~85% participation (40,000-50,000 screening episodes annually). Orthoptists deliver the screening, including presenting vision, cover test, and other tests. Screening failures are referred for eye examinations, including cycloplegic refraction. The study aims to report refractive error data from these examinations (~5,000-6,000 annually) for three years pre- and two years postpandemic and to investigate correlations between myopia and socio-economic factors. Methods: Right eye data from eight Scottish Health Boards (HB) are reported for spherical equivalent refraction (SER) for the years 2013-14, 2014-15, 2015-16, 2020-21, 2021-22. Associations were investigated between the proportion of the sample in each HB with myopia and the proportion of the population in each HB with different levels of deprivation index, classification on a rural/urban continuum, and dwelling type. Results: Refractive error frequency distributions revealed a myopic shift in SER over the five years. Median SER (interquartile range) was, in 2013-14, +1.38D (+0.50 to +2.75); 2014-15, +1.38D (+0.25 to +2.63); 2015-16, +1.38D (+0.50 to +2.75); 2020-21, +1.13D (+0.25 to +2.25); 2021-22, +1.25D (+0.38 to +2.25). The increase in myopia was statistically significant in each of the last two years compared with each of the first three (p<0.0005). The proportion of myopes (≤-0.50D) increased from <7.8% annually 2013-16 to 11.51% in 2020-21, 10.65% in 2021-22 (linear trend: r 2=0.94, p=0.006). Associations between the proportions of children in each HB with myopia and: deprivation index was low and not statistically significant; proportion of population in the most urban environment was high (r2 = 0.79; p=0.003), and proportion of dwellings that were flats/apartments was high (r2 = 0.83, p=0.002). Conclusions: In this predominantly Caucasian population, proportions of children with myopia have increased post-COVID. A strong association exists between myopia and living in flats/apartments and urbanicity, but not with a deprivation index

The MSPTDfast photoplethysmography beat detection algorithm: design, benchmarking, and open-source distribution

Objective: Photoplethysmography is widely used for physiological monitoring, whether in clinical devices such as pulse oximeters, or consumer devices such as smartwatches. A key step in the analysis of photoplethysmogram (PPG) signals is detecting heartbeats. The MSPTD algorithm has been found to be one of the most accurate PPG beat detection algorithms, but is less computationally efficient than other algorithms. Therefore, the aim of this study was to develop a more efficient, open-source implementation of the MSPTD algorithm for PPG beat detection, named MSPTDfast (v.2). Approach: Five potential improvements to MSPTD were identified and evaluated on four datasets. MSPTDfast (v.2) was designed by incorpo- rating each improvement which on its own reduced execution time whilst maintaining a high F1-score. After internal validation, MSPTDfast (v.2) was benchmarked against state-of-the-art beat detection algorithms on four additional datasets. Main results: MSPTDfast (v.2) incorporated two key improvements: pre-processing PPG signals to reduce the sampling frequency to 20 Hz; and only calculating scalogram scales corresponding to heart rates &gt;30 bpm. During internal validation MSPTDfast (v.2) was found to have an execution time of between approximately one-third and one-twentieth of MSPTD, and a comparable F1-score. During benchmarking MSPTDfast (v.2) was found to have the highest F1-score alongside MSPTD, and amongst one of the lowest execution times with only MSPTDfast (v.1), qppgfast and MMPD (v.2) achieving shorter execution times. Significance: MSPTDfast (v.2) is an accurate and efficient PPG beat detection algorithm, available in an open-source Matlab toolbox

GKF-PUAL: A group kernel-free approach to positive-unlabeled learning with variable selection

Variable selection is important for classification of data with many irrelevant predicting variables, but it has not yet been well studied in positive-unlabeled (PU) learning, where classifiers have to be trained without labeled-negative instances. In this paper, we propose a group kernel-free PU classifier with asymmetric loss (GKF-PUAL) to achieve quadratic PU classification with group-lasso regularisation embedded for variable selection. We also propose a five-block algorithm to solve the optimization problem of GKF-PUAL. Our experimental results reveals the superiority of GKF-PUAL in both PU classification and variable selection, improving the baseline PUAL by more than 10% in F1-score across four benchmark datasets and removing over 70% of irrelevant variables on six benchmark datasets. The code for GKF-PUAL is at https://github.com/tkks22123/GKF-PUAL

Comparison of Anatomical and Indication‐Based Diagnostic Reference Levels (DRLs) in Head CT Imaging: Implications for Radiation Dose Management

Introduction: Many diagnostic reference levels (DRLs) in computed tomography (CT) imaging are based mainly on anatomical locations and often overlook variations in radiation exposure due to different clinical indications. While indication‐based DRLs, derived from dose descriptors like volume‐weighted CT dose index (CTDIvol) and dose length product (DLP), are recommended for optimising patient radiation exposure, many studies still use anatomical‐based DRL values. This study is aimed at quantifying the differences between anatomical and indication‐based DRL values in head CT imaging and assessing its implications for radiation dose management. This will support the narrative when explaining the distinction between indication‐based DRLs and anatomical DRLs for patients’ dose management. Methods: Employing a retrospective quantitative study design, we developed and compared anatomical and common indication‐based DRL values using a dataset of head CT scans with similar characteristics. The indications included in the study were brain tumor/intracranial space‐occupying lesion (ISOL), head injury/trauma, stroke, and anatomical examinations. Data analysis was conducted using SPSS Version 29. Results: The findings suggest that using anatomical‐based DLP DRL values for CT head examinations leads to underestimations in the median, 25th percentile, and 75th percentile values of head injury/trauma by 20.2%, 30.0%, and 14.5% in single‐phase CT head procedures. Conversely, for the entire examination, using anatomical‐based DLP DRL as a benchmark for CT stroke DRL overestimates median, 25th percentile, and 75th percentile values by 18.3%, 23.9%, and 13.5%. Brain tumor/ISOL DLP values are underestimated by 62.6%, 60.4%, and 71.8%, respectively. Conclusion:The study highlights that using anatomical DLP DRL values for specific indications in head CT scans can lead to underestimated or overestimated DLP values, making them less reliable for radiation management compared to indication‐based DRLs. Therefore, it is imperative to promote the establishment and use of indication‐based DRLs for more accurate dose management in CT imaging