Wait Up!: Attachment and Sovereign Power.

Sociologists and feminist scholars have, over many decades, characterised attachment as a social construction that functions to support political and gender conservatism. We accept that attachment theory has seen use to these ends and consider recent deployments of attachment theory as justification for a minimal State within conservative political discourse in the UK since 2009. However, we contest that attachment is reducible to its discursive construction. We consider Judith Butler's depiction of the infant attached to an abusive caregiver as a foundation and parallel to the position of the adult citizen subjected to punitive cultural norms and political institutions. We develop and qualify Butler's account, drawing on the insights offered by the work of Lauren Berlant. We also return to Foucault's Psychiatric Power lectures, in which familial relations are situated as an island of sovereign power within the sea of modern disciplinary institutions. These reflections help advance analysis of three important issues: the social and political implications of attachment research; the relationship between disciplinary and sovereign power in the affective dynamic of subjection; and the political and ethical status of professional activity within the psy disciplines.This is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1007/s10767-014-9192-

Spatiotemporal Representation Learning for Short and Long Medical Image Time Series

Analyzing temporal developments is crucial for the accurate prognosis of many medical conditions. Temporal changes that occur over short time scales are key to assessing the health of physiological functions, such as the cardiac cycle. Moreover, tracking longer term developments that occur over months or years in evolving processes, such as age-related macular degeneration (AMD), is essential for accurate prognosis. Despite the importance of both short and long term analysis to clinical decision making, they remain understudied in medical deep learning. State of the art methods for spatiotemporal representation learning, developed for short natural videos, prioritize the detection of temporal constants rather than temporal developments. Moreover, they do not account for varying time intervals between acquisitions, which are essential for contextualizing observed changes. To address these issues, we propose two approaches. First, we combine clip-level contrastive learning with a novel temporal embedding to adapt to irregular time series. Second, we propose masking and predicting latent frame representations of the temporal sequence. Our two approaches outperform all prior methods on temporally-dependent tasks including cardiac output estimation and three prognostic AMD tasks. Overall, this enables the automated analysis of temporal patterns which are typically overlooked in applications of deep learning to medicine

A skeletonization algorithm for gradient-based optimization

The skeleton of a digital image is a compact representation of its topology, geometry, and scale. It has utility in many computer vision applications, such as image description, segmentation, and registration. However, skeletonization has only seen limited use in contemporary deep learning solutions. Most existing skeletonization algorithms are not differentiable, making it impossible to integrate them with gradient-based optimization. Compatible algorithms based on morphological operations and neural networks have been proposed, but their results often deviate from the geometry and topology of the true medial axis. This work introduces the first three-dimensional skeletonization algorithm that is both compatible with gradient-based optimization and preserves an object's topology. Our method is exclusively based on matrix additions and multiplications, convolutional operations, basic non-linear functions, and sampling from a uniform probability distribution, allowing it to be easily implemented in any major deep learning library. In benchmarking experiments, we prove the advantages of our skeletonization algorithm compared to non-differentiable, morphological, and neural-network-based baselines. Finally, we demonstrate the utility of our algorithm by integrating it with two medical image processing applications that use gradient-based optimization: deep-learning-based blood vessel segmentation, and multimodal registration of the mandible in computed tomography and magnetic resonance images.Comment: Accepted at ICCV 202

Metadata-enhanced contrastive learning from retinal optical coherence tomography images

Supervised deep learning algorithms hold great potential to automate screening, monitoring and grading of medical images. However, training performant models has typically required vast quantities of labelled data, which is scarcely available in the medical domain. Self-supervised contrastive frameworks relax this dependency by first learning from unlabelled images. In this work we show that pretraining with two contrastive methods, SimCLR and BYOL, improves the utility of deep learning with regard to the clinical assessment of age-related macular degeneration (AMD). In experiments using two large clinical datasets containing 170,427 optical coherence tomography (OCT) images of 7,912 patients, we evaluate benefits attributed to pretraining across seven downstream tasks ranging from AMD stage and type classification to prediction of functional endpoints to segmentation of retinal layers, finding performance significantly increased in six out of seven tasks with fewer labels. However, standard contrastive frameworks have two known weaknesses that are detrimental to pretraining in the medical domain. Several of the image transformations used to create positive contrastive pairs are not applicable to greyscale medical scans. Furthermore, medical images often depict the same anatomical region and disease severity, resulting in numerous misleading negative pairs. To address these issues we develop a novel metadata-enhanced approach that exploits the rich set of inherently available patient information. To this end we employ records for patient identity, eye position (i.e. left or right) and time series data to indicate the typically unknowable set of inter-image contrastive relationships. By leveraging this often neglected information our metadata-enhanced contrastive pretraining leads to further benefits and outperforms conventional contrastive methods in five out of seven downstream tasks

Explaining variable effects of an adaptable implementation package to promote evidence-based practice in primary care : a longitudinal process evaluation

This study is funded by the National Institute for Health Research (NIHR) [Programme Grants for Applied Research (Grant Reference Number RP-PG-1209-10040)] (https://www.nihr.ac.uk/).Background Implementing evidence-based recommendations is challenging in UK primary care, especially given system pressures and multiple guideline recommendations competing for attention. Implementation packages that can be adapted and hence applied to target multiple guideline recommendations could offer efficiencies for recommendations with common barriers to achievement. We developed and evaluated a package of evidence-based interventions (audit and feedback, educational outreach and reminders) incorporating behaviour change techniques to target common barriers, in two pragmatic trials for four “high impact” indicators: risky prescribing; diabetes control; blood pressure control; and anticoagulation in atrial fibrillation. We observed a significant, cost-effective reduction in risky prescribing but there was insufficient evidence of effect on the other outcomes. We explored the impact of the implementation package on both social processes (Normalisation Process Theory; NPT) and hypothesised determinants of behaviour (Theoretical Domains Framework; TDF). Methods We conducted a prospective multi-method process evaluation. Observational, administrative and interview data collection and analyses in eight primary care practices were guided by NPT and TDF. Survey data from trial and process evaluation practices explored fidelity. Results We observed three main patterns of variation in how practices responded to the implementation package. First, in integration and achievement, the package “worked” when it was considered distinctive and feasible. Timely feedback directed at specific behaviours enabled continuous goal setting, action and review, which reinforced motivation and collective action. Second, impacts on team-based determinants were limited, particularly when the complexity of clinical actions impeded progress. Third, there were delivery delays and unintended consequences. Delays in scheduling outreach further reduced ownership and time for improvement. Repeated stagnant or declining feedback that did not reflect effort undermined engagement. Conclusions Variable integration within practice routines and organisation of care, variable impacts on behavioural determinants, and delays in delivery and unintended consequences help explain the partial success of an adaptable package in primary care.Publisher PDFPeer reviewe

Deep-learning-based clustering of OCT images for biomarker discovery in age-related macular degeneration (Pinnacle study report 4)

Diseases are currently managed by grading systems, where patients are stratified by grading systems into stages that indicate patient risk and guide clinical management. However, these broad categories typically lack prognostic value, and proposals for new biomarkers are currently limited to anecdotal observations. In this work, we introduce a deep-learning-based biomarker proposal system for the purpose of accelerating biomarker discovery in age-related macular degeneration (AMD). It works by first training a neural network using self-supervised contrastive learning to discover, without any clinical annotations, features relating to both known and unknown AMD biomarkers present in 46,496 retinal optical coherence tomography (OCT) images. To interpret the discovered biomarkers, we partition the images into 30 subsets, termed clusters, that contain similar features. We then conduct two parallel 1.5-hour semi-structured interviews with two independent teams of retinal specialists that describe each cluster in clinical language. Overall, both teams independently identified clearly distinct characteristics in 27 of 30 clusters, of which 23 were related to AMD. Seven were recognised as known biomarkers already used in established grading systems and 16 depicted biomarker combinations or subtypes that are either not yet used in grading systems, were only recently proposed, or were unknown. Clusters separated incomplete from complete retinal atrophy, intraretinal from subretinal fluid and thick from thin choroids, and in simulation outperformed clinically-used grading systems in prognostic value. Overall, contrastive learning enabled the automatic proposal of AMD biomarkers that go beyond the set used by clinically established grading systems. Ultimately, we envision that equipping clinicians with discovery-oriented deep-learning tools can accelerate discovery of novel prognostic biomarkers

An adaptable implementation package targeting evidence-based indicators in primary care: a pragmatic cluster-randomised evaluation

Background In primary care, multiple priorities and system pressures make closing the gap between evidence and practice challenging. Most implementation studies focus on single conditions, limiting generalisability. We compared an adaptable implementation package against an implementation control and assessed effects on adherence to four different evidence-based quality indicators. Methods and findings We undertook two parallel, pragmatic cluster-randomised trials using balanced incomplete block designs in general practices in West Yorkshire, England. We used ‘opt-out’ recruitment, and we randomly assigned practices that did not opt out to an implementation package targeting either diabetes control or risky prescribing (Trial 1); or blood pressure (BP) control or anticoagulation in atrial fibrillation (AF) (Trial 2). Within trials, each arm acted as the implementation control comparison for the other targeted indicator. For example, practices assigned to the diabetes control package acted as the comparison for practices assigned to the risky prescribing package. The implementation package embedded behaviour change techniques within audit and feedback, educational outreach, and computerised support, with content tailored to each indicator. Respective patient-level primary endpoints at 11 months comprised the following: achievement of all recommended levels of haemoglobin A1c (HbA1c), BP, and cholesterol; risky prescribing levels; achievement of recommended BP; and anticoagulation prescribing. Between February and March 2015, we recruited 144 general practices collectively serving over 1 million patients. We stratified computer-generated randomisation by area, list size, and pre-intervention outcome achievement. In April 2015, we randomised 80 practices to Trial 1 (40 per arm) and 64 to Trial 2 (32 per arm). Practices and trial personnel were not blind to allocation. Two practices were lost to follow-up but provided some outcome data. We analysed the intention-to-treat (ITT) population, adjusted for potential confounders at patient level (sex, age) and practice level (list size, locality, pre-intervention achievement against primary outcomes, total quality scores, and levels of patient co-morbidity), and analysed cost-effectiveness. The implementation package reduced risky prescribing (odds ratio [OR] 0.82; 97.5% confidence interval [CI] 0.67–0.99, p = 0.017) with an incremental cost-effectiveness ratio of £1,359 per quality-adjusted life year (QALY), but there was insufficient evidence of effect on other primary endpoints (diabetes control OR 1.03, 97.5% CI 0.89–1.18, p = 0.693; BP control OR 1.05, 97.5% CI 0.96–1.16, p = 0.215; anticoagulation prescribing OR 0.90, 97.5% CI 0.75–1.09, p = 0.214). No statistically significant effects were observed in any secondary outcome except for reduced co-prescription of aspirin and clopidogrel without gastro-protection in patients aged 65 and over (adjusted OR 0.62; 97.5% CI 0.39–0.99; p = 0.021). Main study limitations concern our inability to make any inferences about the relative effects of individual intervention components, given the multifaceted nature of the implementation package, and that the composite endpoint for diabetes control may have been too challenging to achieve. Conclusions In this study, we observed that a multifaceted implementation package was clinically and cost-effective for targeting prescribing behaviours within the control of clinicians but not for more complex behaviours that also required patient engagement. Trial registration The study is registered with the ISRCTN registry (ISRCTN91989345)

A randomized double-blinded trial to assess recurrence of systemic allergic reactions following COVID-19 mRNA vaccination

BACKGROUND: Systemic allergic reactions (sARs) following coronavirus disease 2019 (COVID-19) mRNA vaccines were initially reported at a higher rate than after traditional vaccines. OBJECTIVE: We aimed to evaluate the safety of revaccination in these individuals and to interrogate mechanisms underlying these reactions. METHODS: In this randomized, double-blinded, phase 2 trial, participants aged 16 to 69 years who previously reported a convincing sAR to their first dose of COVID-19 mRNA vaccine were randomly assigned to receive a second dose of BNT162b2 (Comirnaty) vaccine and placebo on consecutive days in a blinded, 1:1 crossover fashion at the National Institutes of Health. An open-label BNT162b2 booster was offered 5 months later if the second dose did not result in severe sAR. None of the participants received the mRNA-1273 (Spikevax) vaccine during the study. The primary end point was recurrence of sAR following second dose and booster vaccination; exploratory end points included biomarker measurements. RESULTS: Of 111 screened participants, 18 were randomly assigned to receive study interventions. Eight received BNT162b2 second dose followed by placebo; 8 received placebo followed by BNT162b2 second dose; 2 withdrew before receiving any study intervention. All 16 participants received the booster dose. Following second dose and booster vaccination, sARs recurred in 2 participants (12.5%; 95% CI, 1.6 to 38.3). No sAR occurred after placebo. An anaphylaxis mimic, immunization stress-related response (ISRR), occurred more commonly than sARs following both vaccine and placebo and was associated with higher predose anxiety scores, paresthesias, and distinct vital sign and biomarker changes. CONCLUSIONS: Our findings support revaccination of individuals who report sARs to COVID-19 mRNA vaccines. Distinct clinical and laboratory features may distinguish sARs from ISRRs

QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT; various organs such as the brain, prostate, kidney, and pancreas; and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks.Comment: initial technical repor