Better-than-chance classification for signal detection

The estimated accuracy of a classifier is a random quantity with variability. A common practice in supervised machine learning, is thus to test if the estimated accuracy is significantly better than chance level. This method of signal detection is particularly popular in neuroimaging and genetics. We provide evidence that using a classifier's accuracy as a test statistic can be an underpowered strategy for finding differences between populations, compared to a bona fide statistical test. It is also computationally more demanding than a statistical test. Via simulation, we compare test statistics that are based on classification accuracy, to others based on multivariate test statistics. We find that the probability of detecting differences between two distributions is lower for accuracy-based statistics. We examine several candidate causes for the low power of accuracy-tests. These causes include: the discrete nature of the accuracy-test statistic, the type of signal accuracy-tests are designed to detect, their inefficient use of the data, and their suboptimal regularization. When the purpose of the analysis is the evaluation of a particular classifier, not signal detection, we suggest several improvements to increase power. In particular, to replace V-fold cross-validation with the Leave-One-Out Bootstrap.Development and application of statistical models for medical scientific researc

Computer-vision based method for quantifying rising from chair in Parkinson's disease patients

BACKGROUND: The ability to arise from a sitting to a standing position is often impaired in Parkinson's disease (PD). This impairment is associated with an increased risk of falling, and higher risk of dementia. We propose a novel approach to estimate Movement Disorder Society Unified PD Rating Scale (MDS-UPDRS) ratings for “item 3.9” (arising from chair) using a computer vision-based method, whereby we use clinically informed reasoning to engineer a small number of informative features from high dimensional markerless pose estimation data. METHODS: We analysed 447 videos collected via the KELVIN-PD™ platform, recorded in clinical settings at multiple sites, using commercially available mobile smart devices. Each video showed an examination for item 3.9 of the MDS-UPDRS and had an associated severity rating from a trained clinician on the 5-point scale (0, 1, 2, 3 or 4). The deep learning library OpenPose was used to extract pose estimation key points from each frame of the videos, resulting in time-series signals for each key point. From these signals, features were extracted which capture relevant characteristics of the movement; velocity variation, smoothness, whether the patient used their hands to push themselves up, how stooped the patient was while sitting and how upright the patient was when fully standing. These features were used to train an ordinal classification system (with one class for each of the possible ratings on the UPDRS), based on a series of random forest classifiers. RESULTS: The UPDRS ratings estimated by this system, using leave-one-out cross validation, corresponded exactly to the ratings made by clinicians in 79% of videos, and were within one of those made by clinicians in 100% of cases. The system was able to distinguish normal from Parkinsonian movement with a sensitivity of 62.8% and a specificity of 90.3%. Analysis of misclassified examples highlighted the potential of the system to detect potentially mislabelled data. CONCLUSION: We show that our computer-vision based method can accurately quantify PD patients’ ability to perform the arising from chair action. As far as we are aware this is the first study estimating scores for item 3.9 of the MDS-UPDRS from singular monocular video. This approach can help prevent human error by identifying unusual clinician ratings, and provides promise for such a system being used routinely for clinical assessments, either locally or remotely, with potential for use as stratification and outcome measures in clinical trials

Cannabinoids, cannabis and cannabis-based medicine for pain management: a systematic review of randomised controlled trials

ABSTRACT: Cannabinoids, cannabis, and cannabis-based medicines (CBMs) are increasingly used to manage pain, with limited understanding of their efficacy and safety. We summarised efficacy and adverse events (AEs) of these types of drugs for treating pain using randomised controlled trials: in people of any age, with any type of pain, and for any treatment duration. Primary outcomes were 30% and 50% reduction in pain intensity, and AEs. We assessed risk of bias of included studies, and the overall quality of evidence using GRADE. Studies of &lt;7 and &gt;7 days treatment duration were analysed separately. We included 36 studies (7217 participants) delivering cannabinoids (8 studies), cannabis (6 studies), and CBM (22 studies); all had high and/or uncertain risk of bias. Evidence of benefit was found for cannabis &lt;7 days (risk difference 0.33, 95% confidence interval 0.20-0.46; 2 trials, 231 patients, very low-quality evidence) and nabiximols &gt;7 days (risk difference 0.06, 95% confidence interval 0.01-0.12; 6 trials, 1484 patients, very low-quality evidence). No other beneficial effects were found for other types of cannabinoids, cannabis, or CBM in our primary analyses; 81% of subgroup analyses were negative. Cannabis, nabiximols, and delta-9-tetrahydrocannabinol had more AEs than control. Studies in this field have unclear or high risk of bias, and outcomes had GRADE rating of low- or very low-quality evidence. We have little confidence in the estimates of effect. The evidence neither supports nor refutes claims of efficacy and safety for cannabinoids, cannabis, or CBM in the management of pain.</p

Pregabalin, celecoxib, and their combination for treatment of chronic low-back pain

Background - The efficacy and safety of the association of celecoxib [a selective cyclooxygenase-2 (COX-2) inhibitor] and pregabalin (commonly used to control neuropathic pain), compared with monotherapy of each, were evaluated for the treatment of chronic low-back pain, a condition known to be due to neuropathic as well as nociceptive pain mechanisms. Materials and methods - In this prospective randomized trial, 36 patients received three consecutive 4-week treatment regimes, randomly assigned: celecoxib plus placebo, pregabalin plus placebo, and celecoxib plus pregabalin. All patients were assessed by using a visual analogue scale (VAS, 0\u2013100 mm) and the Leeds Assessment of Neuropathic Symptoms and Signs (LANSS) pain scale by an investigator blinded to the administered pharmacological treatment. Results - Celecoxib and pregabalin were effective in reducing low-back pain when patients were pooled according to LANSS score. The association of celecoxib and pregabalin was more effective than either monotherapy in a mixed population of patients with chronic low-back pain and when data were pooled according to LANSS score. Adverse effects of drug association and monotherapies were similar, with reduced drug consumption in the combined therapy. Conclusions - Combination of celecoxib and pregabalin is more effective than monotherapy for chronic low-back pain, with similar adverse effects

Do “central sensitization” questionnaires reflect measures of nociceptive sensitization or psychological constructs? Protocol for a systematic review

Introduction: Central sensitization (CS) was first defined in animal studies to be increased nociceptive responsiveness due to sensitization of neurons in the central nervous system, usually the result of prolonged nociceptive input or a disease state. Recently, the concept of CS has been adopted in clinical assessments of chronic pain, but its diagnosis in humans has expanded to include the enhancement of a wide range of nociceptive, sensory, and emotional responses. Many poorly understood pain disorders are referred to as “central sensitivity syndrome,” a term associated with a broad range of hypervigilant sensory and emotional responses. Diagnosis often involves a review of medical records and an assessment of behaviour, emotional disposition, and overall sensitivity of a patient. Obviously, these assessments are unable to directly capture the responsiveness of nociceptive neurons. The purpose of this review is to ascertain whether self-report questionnaires associated with central sensitization and the diagnosis of central sensitivity syndrome are associated with enhanced nociceptive responses or whether they more validly measure sensitivity in a broader sense (ie, including emotional responses). Methods: Following the PRISMA guidelines, a detailed search of studies that involve the Central Sensitization Inventory or Pain Sensitivity Questionnaire correlated with either nociceptive sensory tests (quantitative sensory testing) or emotional hypervigilance (anxiety, depression, stress, etc) will be conducted on MEDLINE, PsychINFO, and Web of Science. Perspective: The review is expected to synthesize correlations between sensitivity questionnaires and nociceptive or emotional sensitivity to determine whether these questionnaires reflect a broadened understanding of the term “central sensitization.

A Clinically Interpretable Computer-Vision Based Method for Quantifying Gait in Parkinson's Disease.

Gait is a core motor function and is impaired in numerous neurological diseases, including Parkinson's disease (PD). Treatment changes in PD are frequently driven by gait assessments in the clinic, commonly rated as part of the Movement Disorder Society (MDS) Unified PD Rating Scale (UPDRS) assessment (item 3.10). We proposed and evaluated a novel approach for estimating severity of gait impairment in Parkinson's disease using a computer vision-based methodology. The system we developed can be used to obtain an estimate for a rating to catch potential errors, or to gain an initial rating in the absence of a trained clinician-for example, during remote home assessments. Videos (n=729) were collected as part of routine MDS-UPDRS gait assessments of Parkinson's patients, and a deep learning library was used to extract body key-point coordinates for each frame. Data were recorded at five clinical sites using commercially available mobile phones or tablets, and had an associated severity rating from a trained clinician. Six features were calculated from time-series signals of the extracted key-points. These features characterized key aspects of the movement including speed (step frequency, estimated using a novel Gamma-Poisson Bayesian model), arm swing, postural control and smoothness (or roughness) of movement. An ordinal random forest classification model (with one class for each of the possible ratings) was trained and evaluated using 10-fold cross validation. Step frequency point estimates from the Bayesian model were highly correlated with manually labelled step frequencies of 606 video clips showing patients walking towards or away from the camera (Pearson's r=0.80, p<0.001). Our classifier achieved a balanced accuracy of 50% (chance = 25%). Estimated UPDRS ratings were within one of the clinicians' ratings in 95% of cases. There was a significant correlation between clinician labels and model estimates (Spearman's ρ=0.52, p<0.001). We show how the interpretability of the feature values could be used by clinicians to support their decision-making and provide insight into the model's objective UPDRS rating estimation. The severity of gait impairment in Parkinson's disease can be estimated using a single patient video, recorded using a consumer mobile device and within standard clinical settings; i.e., videos were recorded in various hospital hallways and offices rather than gait laboratories. This approach can support clinicians during routine assessments by providing an objective rating (or second opinion), and has the potential to be used for remote home assessments, which would allow for more frequent monitoring

Patient-reported-outcomes in subjects with painful lumbar or cervical radiculopathy treated with pregabalin: evidence from medical practice in primary care settings

The objective of this study was to evaluate the effect of pregabalin in painful cervical or lumbosacral radiculopathy treated in Primary Care settings under routine clinical practice. An observational, prospective 12-week secondary analysis was carried-out. Male and female above 18 years, naïve to PGB, with refractory chronic pain secondary to cervical/lumbosacral radiculopathy were enrolled. SF-MPQ, Sheehan Disability Inventory, MOS Sleep Scale, Hospital Anxiety and Depression Scale and the EQ-5D were administered. A total of 490 (34%) patients were prescribed PGB-monotherapy, 702 (48%) received PGB add-on, and 159 (11%) were administered non-PGB drugs. After 12 weeks, significant improvements in pain, associated symptoms of anxiety, depression and sleep disturbances, general health; and level of disability were observed in the three groups, being significantly greater in PGB groups. In routine medical practice, monotherapy or add-on pregabalin is associated with substantial pain alleviation and associated symptoms improvements in painful cervical or lumbosacral radiculopathy