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Machine learning (ML) has attracted much attention with the hope that it could make use of large, routinely collected datasets and deliver accurate personalised prognosis. The aim of this systematic review is to identify and critically appraise the reporting and developing of ML models for predicting outcomes after stroke. We searched PubMed and Web of Science from 1990 to March 2019, using previously published search filters for stroke, ML, and prediction models. We focused on structured clinical data, excluding image and text analysis. This review was registered with PROSPERO (CRD42019127154). Eighteen studies were eligible for inclusion. Most studies reported less than half of the terms in the reporting quality checklist. The most frequently predicted stroke outcomes were mortality (7 studies) and functional outcome (5 studies). The most commonly used ML methods were random forests (9 studies), support vector machines (8 studies), decision trees (6 studies), and neural networks (6 studies). The median sample size was 475 (range 70-3184), with a median of 22 predictors (range 4-152) considered. All studies evaluated discrimination with thirteen using area under the ROC curve whilst calibration was assessed in three. Two studies performed external validation. None described the final model sufficiently well to reproduce it. The use of ML for predicting stroke outcomes is increasing. However, few met basic reporting standards for clinical prediction tools and none made their models available in a way which could be used or evaluated. Major improvements in ML study conduct and reporting are needed before it can meaningfully be considered for practice.

Societal Impact Statement Humankind is facing both climate and biodiversity crises. This article proposes the foundations of a scheme that offers tradable credits for combined aboveground and soil carbon and biodiversity. Multidiversity—as estimated based on high‐throughput molecular identification of soil meiofauna, fungi, bacteria, protists, plants and other organisms shedding DNA into soil, complemented by acoustic and video analyses of aboveground macrobiota—offers a cost‐effective method that captures much of the terrestrial biodiversity. Such a voluntary crediting system would increase the quality of carbon projects and contribute funding for delivering the Kunming‐Montreal Global Biodiversity Framework. Summary Carbon crediting and land offsets for biodiversity protection have been developed to tackle the challenges of increasing greenhouse gas emissions and the loss of global biodiversity. Unfortunately, these two mechanisms are not optimal when considered separately. Focusing solely on carbon capture—the primary goal of most carbon‐focused crediting and offsetting commitments—often results in the establishment of non‐native, fast‐growing monocultures that negatively affect biodiversity and soil‐related ecosystem services. Soil contributes a vast proportion of global biodiversity and contains traces of aboveground organisms. Here, we outline a carbon and biodiversity co‐crediting scheme based on the multi‐kingdom molecular and carbon analyses of soil samples, along with remote sensing estimation of aboveground carbon as well as video and acoustic analyses‐based monitoring of aboveground macroorganisms. Combined, such a co‐crediting scheme could help halt biodiversity loss by incentivising industry and governments to account for biodiversity in carbon sequestration projects more rigorously, explicitly and equitably than they currently do. In most cases, this would help prioritise protection before restoration and help promote more socially and environmentally sustainable land stewardship towards a ‘nature positive’ future. Inimkond ägab nii kliima‐ kui ka elurikkuse kriiside all. Selles artiklis pakume välja kaubeldava süsiniku ja elurikkuse kooskrediteerimise skeemi, kus eri organismirühmade (selgrootud loomad, seened, bakterid, protistid, taimed) kaalutud liigirikkus mullaproovides on peamine elurikkuse näitaja. Molekulaarsete meetodite abil läbiviidud mulla DNA uuringud koos video ja akustilise materjaliga võimaldavad kulutõhusalt hinnata kogu ökosüsteemi elurikkust. Siin väljapakutud kooskrediteerimise põhimõte võimaldab tõhustada süsinikuprojekte ja rahastada looduskaitset. Humankind is facing both climate and biodiversity crises. This article proposes the foundations of a scheme that offers tradable credits for combined aboveground and soil carbon and biodiversity. Multidiversity—as estimated based on high‐throughput molecular identification of soil meiofauna, fungi, bacteria, protists, plants and other organisms shedding DNA into soil, complemented by acoustic and video analyses of aboveground macrobiota—offers a cost‐effective method that captures much of the terrestrial biodiversity. Such a voluntary crediting system would increase the quality of carbon projects and contribute funding for delivering the Kunming‐Montreal Global Biodiversity Framework.

Objectives The purpose of this study was to build a deep learning model to derive labels from neuroradiology reports and assign these to the corresponding examinations, overcoming a bottleneck to computer vision model development. Methods Reference-standard labels were generated by a team of neuroradiologists for model training and evaluation. Three thousand examinations were labelled for the presence or absence of any abnormality by manually scrutinising the corresponding radiology reports (‘reference-standard report labels’); a subset of these examinations ( n = 250) were assigned ‘reference-standard image labels’ by interrogating the actual images. Separately, 2000 reports were labelled for the presence or absence of 7 specialised categories of abnormality (acute stroke, mass, atrophy, vascular abnormality, small vessel disease, white matter inflammation, encephalomalacia), with a subset of these examinations ( n = 700) also assigned reference-standard image labels. A deep learning model was trained using labelled reports and validated in two ways: comparing predicted labels to (i) reference-standard report labels and (ii) reference-standard image labels. The area under the receiver operating characteristic curve (AUC-ROC) was used to quantify model performance. Accuracy, sensitivity, specificity, and F1 score were also calculated. Results Accurate classification (AUC-ROC > 0.95) was achieved for all categories when tested against reference-standard report labels. A drop in performance (ΔAUC-ROC > 0.02) was seen for three categories (atrophy, encephalomalacia, vascular) when tested against reference-standard image labels, highlighting discrepancies in the original reports. Once trained, the model assigned labels to 121,556 examinations in under 30 min. Conclusions Our model accurately classifies head MRI examinations, enabling automated dataset labelling for downstream computer vision applications. Key Points • Deep learning is poised to revolutionise image recognition tasks in radiology; however, a barrier to clinical adoption is the difficulty of obtaining large labelled datasets for model training. • We demonstrate a deep learning model which can derive labels from neuroradiology reports and assign these to the corresponding examinations at scale, facilitating the development of downstream computer vision models. • We rigorously tested our model by comparing labels predicted on the basis of neuroradiology reports with two sets of reference-standard labels: (1) labels derived by manually scrutinising each radiology report and (2) labels derived by interrogating the actual images.

BackgroundPreoperative peripheral nerve block placement can involve both procedural pain and psychological distress, which practitioners treat using both sedation and education/reassurance. The experience of pain may be potently modulated by baseline pain catastrophizing (presence of rumination, magnification, and helplessness). This randomized clinical trial assessed whether the treatment effect of sedation vs education/reassurance on procedural nerve block pain and satisfaction varied for patients with different baseline pain catastrophizing scores.MethodsAt baseline, patients completed the Pain Catastrophizing Scale (PCS), were stratified into low-PCS (<10) or high-PCS (≥10) groups, and then randomized to receive sedation or education/reassurance during nerve block placement. Patients reported maximum and average procedural pain and satisfaction, immediately after the procedure and recalled in the postanesthesia care unit (PACU). Generalized estimating equations examined main effects of treatment and baseline PCS group on maximum procedural pain and the interaction between them.ResultsMaximal procedural pain immediately after the procedure was similar between treatment groups (n=72), but a significant treatment×PCS group interaction (B=0.8, 95% CI (0.04, 1.5), p=0.04) indicated that among patients with high PCS, sedation was associated with less pain (2.3±2.2 vs 4.3±2.5, p=0.01). Exploratory findings indicate sedation being associated with lower recalled procedural pain in PACU than education/reassurance (0.3±0.7 vs 2.2±2.4, p<0.001), and education being associated with higher satisfaction among those with lower PCS.DiscussionOur findings suggest that patients with high PCS may disproportionately benefit from sedation during nerve block, reporting less pain, whereas patients with low PCS may have a slight preference for education/reassurance, reporting higher satisfaction.Trial registration numberNCT05579509.

The formation of rare-gas excimers in a dc supersonic jet discharge and the resulting spectra were studied in detail. Emission spectra of He $\\sbsp{2}{\\*},$Ne $\\sbsp{2}{\\*},$Ar $\\sbsp{2}{\\*},$Kr $\\sbsp{2}{\\*}$and Xe $\\sbsp{2}{\\*}$characterized by two vibronic bands were recorded in the 50 to 200 nm region. These bands, known as the \"first continuum\" and the \"second continuum\", arise due to transitions from high and low vibrational levels, respectively, of the excimer states, to the repulsive potential of the ground state. Intense first and second continua were observed for Ar $\\sbsp{2}{\\*},$Kr $\\sbsp{2}{\\*}$and Xe $\\sbsp{2}{\\*}$and much weaker bands for He $\\sbsp{2}{\\*}$and Ne $\\sbsp{2}{\\*}.$Detailed investigations were therefore carried out with the three heavier rare-gas dimers, with emphasis on Ar $\\sbsp{2}{\\*}.$Once the presence of rare-gas excimers was confirmed, experiments were conducted to measure effects on the spectra by optically pumping the discharge with laser radiation tuned to atomic transitions in the 2p $-$ 1s manifold. Intensity decreases of the order of 10% were observed in the excimer bands when the laser radiation was tuned to transitions originating from the atomic 1s $\\sb5$level, the precursor to the A $\\sp3\\Sigma\\sbsp{\\rm u}{+}$excimer state. Such decreases were generally not observed when the radiation was tuned to transitions originating from the 1s $\\sb2,$1s $\\sb3$and, in particular, the 1s $\\sb4$state, which is the atomic precursor to the B $\\sp1\\Sigma\\sbsp{\\rm u}{+}$state. These results established that the$\\rm A\\sp3\\Sigma\\sbsp{u}{+}$(rather than the$\\rm B\\sp1\\Sigma\\sbsp{u}{+})$excimer state was responsible for the emission bands. Subsequently, absorption measurements on the atomic and excimer species were used to determine population densities, and to show that these excited species were concentrated near the shock wave, rather than in the isentropic core of the jet discharge. Rate equations were developed to explain the intensity changes of the atomic resonance lines and excimer bands caused by optical pumping. A comparison of calculated and experimental results showed that collisional mixing between atoms in the 1s $\\sb4$and 1s $\\sb5$levels is also an important process in formation of rare-gas excimers.

Labelling large datasets for training high-capacity neural networks is a major obstacle to the development of deep learning-based medical imaging applications. Here we present a transformer-based network for magnetic resonance imaging (MRI) radiology report classification which automates this task by assigning image labels on the basis of free-text expert radiology reports. Our model's performance is comparable to that of an expert radiologist, and better than that of an expert physician, demonstrating the feasibility of this approach. We make code available online for researchers to label their own MRI datasets for medical imaging applications.