Age and Sex Differences in Pediatric Neuropathic Pain and Complex Regional Pain Syndrome: A Scoping Review.

BACKGROUND: Age and sex differences may exist in the frequency (incidence, prevalence) or symptoms of neuropathic pain (NP) and complex regional pain syndrome (CRPS) due to biopsychosocial factors (eg, neurodevelopment, physiological and hormonal changes, psychosocial differences) that evolve through childhood and adolescence. Age and sex differences may have implications for evaluating screening and diagnostic tools and treatment interventions. OBJECTIVE: To map the existing literature on pediatric NP and CRPS with respect to age and sex distributions, and age and sex differences in symptomology and frequency. METHODS: A scoping literature review was conducted. Databases were searched from inception to January 2023. Data were collected on study design, setting, demographics, and age and sex differences in frequency and symptoms. RESULTS: Eighty-seven studies were included. Distribution of participants with CRPS (n=37 studies) was predominantly early adolescence (10 to 14 y) and female sex, while NP (n=42 studies) was most commonly reported throughout adolescence (10 to 19 y) in both sexes. Forty-one studies examined age and sex differences in frequency; 6 studies reported higher frequency in adolescence. Very few studies (n=11) examined differences in symptomology. DISCUSSION: Large epidemiological studies are required to further understand age and sex differences in frequency of pediatric NP and CRPS. Age and sex differences must be considered when evaluating screening and diagnostic tools and treatment interventions to ensure relevance and validity to both sexes and across ages. Validated tools will improve understanding of age-dependent and sex-dependent differences in symptoms, pathophysiology, and psychosocial impact of pediatric NP and CRPS.

Evaluating the validity of a functional electrical stimulation clinical decision making tool: A qualitative study.

Introduction: Following central nervous system damage, the recovery of motor function is a priority. For some neurological populations, functional electrical stimulation (FES) is recommended in best practice guidelines for neurorehabilitation. However, limited resources exist to guide FES application, despite clinicians reporting that a lack of FES knowledge prevents use in clinical practice. The FES Clinical Decision Making Tool was developed to assist clinicians with FES application and translation into clinical practice. The purpose of this study was to evaluate the content validity of the Tool from the perspectives of Canadian physical and occupational therapists using FES in neurorehabilitation. Methods: Thirteen participants (twelve women, one man), aged 40.5 ± 10.3 years, participated in individual semi-structured interviews to explore their clinical decision making experiences when applying FES and to evaluate the content validity (i.e., appropriateness, comprehensibility, and comprehensiveness) of the Tool. Interviews were analyzed using a qualitative conventional content analysis following the DEPICT model. Results: Three themes were identified. 1) Clinician context influences FES usage. Participants' experiences with FES use varied and application was influenced by treatment goals. 2) Parameter selection in clinical practice. Participants identified decision-making strategies and the challenges of parameter selection. 3) With modifications, the Tool is a valid resource to inform FES applications. Participants discussed its strengths, limitations, and suggested changes. While the Tool is useful, a more extensive resource (e.g., appendix) for the Tool is warranted. Discussion: A revised Tool was created to improve its comprehensiveness and comprehensibility. Thus, the Tool is a valid resource for applying FES in neurorehabilitation.

Serological evidence of a pararubulavirus and a betacoronavirus in the geographically isolated Christmas Island flying-fox (Pteropus natalis).

Due to their geographical isolation and small populations, insular bats may not be able to maintain acute immunizing viruses that rely on a large population for viral maintenance. Instead, endemic transmission may rely on viruses establishing persistent infections within hosts or inducing only short-lived neutralizing immunity. Therefore, studies on insular populations are valuable for developing broader understanding of viral maintenance in bats. The Christmas Island flying-fox (CIFF; Pteropus natalis) is endemic on Christmas Island, a remote Australian territory, and is an ideal model species to understand viral maintenance in small, geographically isolated bat populations. Serum or plasma (n = 190), oral swabs (n = 199), faeces (n = 31), urine (n = 32) and urine swabs (n = 25) were collected from 228 CIFFs. Samples were tested using multiplex serological and molecular assays, and attempts at virus isolation to determine the presence of paramyxoviruses, betacoronaviruses and Australian bat lyssavirus. Analysis of serological data provides evidence that the species is maintaining a pararubulavirus and a betacoronavirus. There was little serological evidence supporting the presence of active circulation of the other viruses assessed in the present study. No viral nucleic acid was detected and no viruses were isolated. Age-seropositivity results support the hypothesis that geographically isolated bat populations can maintain some paramyxoviruses and coronaviruses. Further studies are required to elucidate infection dynamics and characterize viruses in the CIFF. Lastly, apparent absence of some pathogens could have implications for the conservation of the CIFF if a novel disease were introduced into the population through human carriage or an invasive species. Adopting increased biosecurity protocols for ships porting on Christmas Island and for researchers and bat carers working with flying-foxes are recommended to decrease the risk of pathogen introduction and contribute to the health and conservation of the species.

Psycho-sensory relationships in chronic pain.

BACKGROUND: Psychological variables contribute to pain- and injury-related outcomes. We examined the hypothesis that anatomical spread and intensity of persistent pain relate to anxiety-related variables: generalised anxiety, fear of pain and pain catastrophising. METHODS: An online survey was used to gather data from 413 women with persistent pain (low back pain, n = 139; fibromyalgia syndrome, n = 95; neck pain, n = 55; whiplash, n = 41; rheumatoid arthritis, n = 37; migraine, n = 46). The spread and intensity of pain were assessed using the McGill pain chart and a Numerical Rating Scale. A Bayesian Structural Equation Model assessed if the intensity and spread of pain increased with anxiety-related variables. Men were also surveyed (n = 80), but the sample size was only sufficient for analysing if their data were consistent with the model for women. RESULTS: Across subgroups of women, one standard deviation increase in catastrophising, generalised anxiety and fear corresponded to 27%, 7% and -1% additional pain areas and a 1.1, 0 and -0.1 change in pain intensity (on 0-10 scale), respectively. Overall, our clinical significance criterion - a 30% shift in pain variable in relation to one standard deviation increase in psychological variable - was not met. However, in subgroups it was met for pain spread (low back pain, neck pain and migraine) and pain intensity (migraine and neck pain) in relation to pain catastrophising. The model generally had low goodness-of-fit to men. CONCLUSION: These data support a meaningful relationship between some anxiety-related variables and pain in women for some conditions. Since the model did not consistently fit the men, we may conclude that the relationships are moderated by sex. Clinician attention to psychological variables as potential contributing factors can be justified; however, research is needed to understand the relationship and whether psychological treatment can reduce pain.

Commentary response: Smartphone use and parenting: re-stratifying the multiverse for families of young children.

Concerns have been raised that smartphones may harm children and families. Arguably, risk-driven discourses are not always evidence-based. This is a problem, because blanket assumptions of risk drowns out nuanced empirical questions of what constitutes "good" parenting when it comes to smartphone use, and for whom. Here we outline three logical missteps which have contributed to the deficit zeitgeist-ignoring context, misinterpreting effect, and conflation. Further, we speak to questions about parents of young children, by refocusing our multiverse analysis on 800+ parents. We ask- where are the links between parental phone use and parenting? Are these robust versus frail or positive versus negative? After re-examining our 84 analytic choices (adopting existing measures), patterns revealed fragility in this case. The few findings that did emerge implicated technoference, not smartphone use, in relation to negative parenting. We encourage continued rigorous and scientific dialogue, to accrue good evidence for families and children.

Modeling individualized trajectories of symptom change to improve feedback procedures in psychotherapy.

OBJECTIVE: Psychotherapy feedback compares an individual's treatment progress to the averaged progress of all clients to determine whether their progress is sufficient. However, this can invoke the ecological fallacy if the average trajectory combines heterogenous trajectories with clinically meaningful differences. The current study, instead, explored individualized trajectories of change in psychotherapy and examined the feasibility of using these individualized models to predict clients' future trajectories. METHOD: The Outcome Questionnaire-45 was completed at each session of psychotherapy by 398 adults (16-83 years; Mage = 36.01 years) attending two Australian psychology training clinics. Up to seven Bayesian, polynomial curve-linear regression models were fit and compared for each client. For a hold-out sample (N = 50), models were fit sequentially for each client in five-session increments and used to generate tailored predictions of expected progress at the next five sessions. RESULTS: Constant (no change) and linear (steady change) were the most common shapes of change; only 3% of clients experienced negatively accelerating improvement, as per the expected treatment response curve used in current feedback procedures. Three exemplars demonstrated how individualized modeling and predictions could be utilized in clinical practice to provide richer, more nuanced feedback to psychotherapists about client progress and likely prognosis. CONCLUSIONS: This study was the first to model individualized trajectories of symptom change across psychotherapy and in doing so, uncovered substantial heterogeneity in client trajectories. This means that averaged trajectories are likely to be misleading. Individualized modeling could complement current feedback procedures. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Metal-Organic Framework-Plant Nanobiohybrids as Living Sensors for On-Site Environmental Pollutant Detection.

Photoluminescent metal-organic frameworks (MOFs) were grown in a living plant (Syngonium podophyllum) via immersing their roots in an aqueous solution of disodium terephthalate and terbium chloride hexahydrate sequentially for 12 h without affecting their viability. Then, app-assisted living MOF-plant nanobiohybrids were used for the detection of various toxic metal ions and organic pollutants. Their performance and sensing mechanism were also evaluated. The results demonstrated that the living plants served as self-powered preconcentrators via their passive fluid transport systems and accumulated the pollutants around the embedded MOFs, resulting in relative changes in fluorescence intensity. Therefore, the living MOF-plant nanobiohybrids initiate superior selectivity and sensitivity (0.05-0.5 µM) in water for Ag+, Cd2+, and aniline with a "turn-up" fluorescence response and for Fe3+ and Cu2+ with "turn-down" fluorescence response in the linear range of 0.05-10 µM with excellent precision and accuracy of 5 and 10%, respectively. With the easy-to-read visual signals under ultraviolet light, the app translates plant luminescent signals into digital information on a smartphone for on-site monitoring of environmental pollutants with high sensitivity and specificity. These results suggest that interfacing synthetic and living materials may contribute to the development of smart sensors for on-site environmental pollutant sensing with high accuracy.

Tuning into the real effect of smartphone use on parenting: a multiverse analysis.

BACKGROUND: Concerns have been raised regarding the potential negative impacts of parents' smartphone use on the parent-child relationship. A scoping literature review indicated inconsistent effects, arguably attributable to different conceptualizations of parent phone use and conflation of phone use with technological interference. METHODS: Based on a sample of n = 3, 659 parents collected in partnership with a national public broadcaster, we conducted a multiverse analysis. We explored 84 different analytic choices to address whether associations were weak versus robust, and provide clearer direction for measurement, theory, and practice. Effects were assessed in relation to p values, effect sizes, and AIC; we further conducted a meta-analytic sensitivity check. RESULTS: Direct associations between smartphone use and parenting were relatively weak and mixed. Instead, the relation between use and parenting depended on level of technological interference. This pattern was particularly robust for family displacement. At low levels of displacing time with family using technology, more smartphone use was associated with better (not worse) parenting. CONCLUSIONS: Our results indicate fragility in findings of risks for parental smartphone use on parenting; there were few concerns in this regard. Rather, at low levels of technological interference, more phone use was associated with higher parenting quality. Scholars should avoid generalized narratives of family risk and seek to uncover real effects of smartphone use on family outcomes across diverse households and contexts.

Quantifying conditional probability tables in Bayesian networks: Bayesian regression for scenario-based encoding of elicited expert assessments on feral pig habitat.

Bayesian networks are now widespread for modelling uncertain knowledge. They graph probabilistic relationships, which are quantified using conditional probability tables (CPTs). When empirical data are unavailable, experts may specify CPTs. Here we propose novel methodology for quantifying CPTs: a Bayesian statistical approach to both elicitation and encoding of expert-specified probabilities, in a way that acknowledges their uncertainty. We illustrate this new approach using a case study describing habitat most at risk from feral pigs. For complicated CPTs, it is difficult to elicit all scenarios (CPT entries). Like the CPT Calculator software program, we ask about a few scenarios (e.g. under a one-factor-at-a-time design) to reduce the experts' workload. Unlike CPT Calculator, we adopt a global rather than local regression to 'fill out' CPT entries. Unlike other methods for scenario-based elicitation for regression, we capture uncertainty about each probability in a sequence that explicitly controls biases and enhances interpretation. Furthermore, to utilize all elicited information, we introduce Bayesian rather than Classical generalised linear modelling (GLM). For large CPTs (e.g. >3 levels per parent) we show Bayesian GLM supports richer inference, particularly on interactions, even with few scenarios, providing more information regarding accuracy of encoding.

Does feedback improve psychotherapy outcomes compared to treatment-as-usual for adults and youth?

Objective: Client-informed outcome feedback has consistently been shown to enhance psychotherapy outcomes for adults, particularly for clients at risk of treatment failure. However, there is a paucity of studies examining feedback in youth psychotherapy. Specifically, there is no research examining the feedback effect of the Youth-Outcome Questionnaire [Burlingame, G. M., Wells, M. G., & Lambert, M. J. (1996). The youth outcome questionnaire. Stevenson, MD: American Professional Credentialing Services.], despite the dominance of the adult version of the measure (Outcome Questionnaire-45 [Lambert, M. J., & Burlingame, G. M. (1996). Outcome questionnaire 45.2. Wilmington, DE: American Professional Credentialing Services.]) in adult feedback studies. Method: The effectiveness results for adult (N = 398) and youth clients (N = 397) attending psychotherapy at two psychology training clinics are presented and benchmarked against treatment-as-usual (for adults and youth) and feedback (for adults). Results: Psychotherapy with a feedback-informed approach was more effective than treatment-as-usual benchmarks, with 50% of adults and 64% of youth significantly improving after psychotherapy. Rates of adult improvement were similar to feedback-informed benchmarks, although the current sample had a higher rate of deterioration. There are no previously identified feedback-informed benchmarks for the Y-OQ, making this sample the first benchmark for future studies. Conclusions: Results support the benefits of feedback at enhancing psychotherapy outcomes for adults, and replicate this finding in a youth sample. Results also replicate that trainee psychotherapists can be as effective as licenced psychotherapists.

Effects of exposure to ambient ultrafine particles on respiratory health and systemic inflammation in children.

It is known that ultrafine particles (UFP, particles smaller than 0.1 µm) can penetrate deep into the lungs and potentially have adverse health effects. However, epidemiological data on the health effects of UFP is limited. Therefore, our objective was to test the hypothesis that exposure to UFPs is associated with respiratory health status and systemic inflammation among children aged 8 to 11 years. We conducted a cross-sectional study among 655 children (43.3% male) attending 25 primary (elementary) schools in the Brisbane Metropolitan Area, Australia. Ultrafine particle number concentration (PNC) was measured at each school and modelled at homes using Land Use Regression to derive exposure estimates. Health outcomes were respiratory symptoms and diagnoses, measured by parent-completed questionnaire, spirometric lung function, exhaled nitric oxide (FeNO), and serum C reactive protein (CRP). Exposure-response models, adjusted for potential personal and environmental confounders measured at the individual, home and school level, were fitted using Bayesian methods. PNC was not independently associated with respiratory symptoms, asthma diagnosis or spirometric lung function. However, PNC was positively associated with an increase in CRP (1.188-fold change per 1000 UFP cm-3 day/day (95% credible interval 1.077 to 1.299)) and an increase in FeNO among atopic participants (1.054 fold change per 1000 UFP cm-3 day/day (95% CrI 1.005 to 1.106)). UFPs do not affect respiratory health outcomes in children but do have systemic effects, detected here in the form of a positive association with a biomarker for systemic inflammation. This is consistent with the known propensity of UFPs to penetrate deep into the lung and circulatory system.

Characterising Uncertainty in Expert Assessments: Encoding Heavily Skewed Judgements.

When limited or no observed data are available, it is often useful to obtain expert knowledge about parameters of interest, including point estimates and the uncertainty around these values. However, it is vital to elicit this information appropriately in order to obtain valid estimates. This is particularly important when the experts' uncertainty about these estimates is strongly skewed, for instance when their best estimate is the same as the lowest value they consider possible. Also this is important when interest is in the aggregation of elicited values. In this paper, we compare alternative distributions for describing such estimates. The distributions considered include the lognormal, mirror lognormal, Normal and scaled Beta. The case study presented here involves estimation of the number of species in coral reefs, which requires eliciting counts within broader taxonomic groups, with highly skewed uncertainty estimates. This paper shows substantial gain in using the scaled Beta distribution, compared with Normal or lognormal distributions. We demonstrate that, for this case study on counting species, applying the novel encoding methodology developed in this paper can facilitate the acquisition of more rigorous estimates of (hierarchical) count data and credible bounds. The approach can also be applied to the more general case of enumerating a sampling frame via elicitation.

Species richness on coral reefs and the pursuit of convergent global estimates.

Global species richness, whether estimated by taxon, habitat, or ecosystem, is a key biodiversity metric. Yet, despite the global importance of biodiversity and increasing threats to it (e.g., we are no better able to estimate global species richness now than we were six decades ago. Estimates of global species richness remain highly uncertain and are often logically inconsistent. They are also difficult to validate because estimation of global species richness requires extrapolation beyond the number of species known. Given that somewhere between 3% and >96% of species on Earth may remain undiscovered, depending on the methods used and the taxa considered, such extrapolations, especially from small percentages of known species, are likely to be highly uncertain. An alternative approach is to estimate all species, the known and unknown, directly. Using expert taxonomic knowledge of the species already described and named, those already discovered but not yet described and named, and those still awaiting discovery, we estimate there to be 830,000 (95% credible limits: 550,000-1,330,000) multi-cellular species on coral reefs worldwide, excluding fungi. Uncertainty surrounding this estimate and its components were often strongly skewed toward larger values, indicating that many more species on coral reefs is more plausible than many fewer. The uncertainties revealed here should guide future research toward achieving convergence in global species richness estimates for coral reefs and other ecosystems via adaptive learning protocols whereby such estimates can be tested and improved, and their uncertainties reduced, as new knowledge is acquired.

Understanding uncertainties in non-linear population trajectories: a Bayesian semi-parametric hierarchical approach to large-scale surveys of coral cover.

Recently, attempts to improve decision making in species management have focussed on uncertainties associated with modelling temporal fluctuations in populations. Reducing model uncertainty is challenging; while larger samples improve estimation of species trajectories and reduce statistical errors, they typically amplify variability in observed trajectories. In particular, traditional modelling approaches aimed at estimating population trajectories usually do not account well for nonlinearities and uncertainties associated with multi-scale observations characteristic of large spatio-temporal surveys. We present a Bayesian semi-parametric hierarchical model for simultaneously quantifying uncertainties associated with model structure and parameters, and scale-specific variability over time. We estimate uncertainty across a four-tiered spatial hierarchy of coral cover from the Great Barrier Reef. Coral variability is well described; however, our results show that, in the absence of additional model specifications, conclusions regarding coral trajectories become highly uncertain when considering multiple reefs, suggesting that management should focus more at the scale of individual reefs. The approach presented facilitates the description and estimation of population trajectories and associated uncertainties when variability cannot be attributed to specific causes and origins. We argue that our model can unlock value contained in large-scale datasets, provide guidance for understanding sources of uncertainty, and support better informed decision making.

What is an expert? A systems perspective on expertise.

Expert knowledge is a valuable source of information with a wide range of research applications. Despite the recent advances in defining expert knowledge, little attention has been given to how to view expertise as a system of interacting contributory factors for quantifying an individual's expertise. We present a systems approach to expertise that accounts for many contributing factors and their inter-relationships and allows quantification of an individual's expertise. A Bayesian network (BN) was chosen for this purpose. For illustration, we focused on taxonomic expertise. The model structure was developed in consultation with taxonomists. The relative importance of the factors within the network was determined by a second set of taxonomists (supra-experts) who also provided validation of the model structure. Model performance was assessed by applying the model to hypothetical career states of taxonomists designed to incorporate known differences in career states for model testing. The resulting BN model consisted of 18 primary nodes feeding through one to three higher-order nodes before converging on the target node (Taxonomic Expert). There was strong consistency among node weights provided by the supra-experts for some nodes, but not others. The higher-order nodes, "Quality of work" and "Total productivity", had the greatest weights. Sensitivity analysis indicated that although some factors had stronger influence in the outer nodes of the network, there was relatively equal influence of the factors leading directly into the target node. Despite the differences in the node weights provided by our supra-experts, there was good agreement among assessments of our hypothetical experts that accurately reflected differences we had specified. This systems approach provides a way of assessing the overall level of expertise of individuals, accounting for multiple contributory factors, and their interactions. Our approach is adaptable to other situations where it is desirable to understand components of expertise.

Eliciting expert knowledge in conservation science.

Expert knowledge is used widely in the science and practice of conservation because of the complexity of problems, relative lack of data, and the imminent nature of many conservation decisions. Expert knowledge is substantive information on a particular topic that is not widely known by others. An expert is someone who holds this knowledge and who is often deferred to in its interpretation. We refer to predictions by experts of what may happen in a particular context as expert judgments. In general, an expert-elicitation approach consists of five steps: deciding how information will be used, determining what to elicit, designing the elicitation process, performing the elicitation, and translating the elicited information into quantitative statements that can be used in a model or directly to make decisions. This last step is known as encoding. Some of the considerations in eliciting expert knowledge include determining how to work with multiple experts and how to combine multiple judgments, minimizing bias in the elicited information, and verifying the accuracy of expert information. We highlight structured elicitation techniques that, if adopted, will improve the accuracy and information content of expert judgment and ensure uncertainty is captured accurately. We suggest four aspects of an expert elicitation exercise be examined to determine its comprehensiveness and effectiveness: study design and context, elicitation design, elicitation method, and elicitation output. Just as the reliability of empirical data depends on the rigor with which it was acquired so too does that of expert knowledge.

Bayesian classification and regression trees for predicting incidence of cryptosporidiosis.

BACKGROUND: Classification and regression tree (CART) models are tree-based exploratory data analysis methods which have been shown to be very useful in identifying and estimating complex hierarchical relationships in ecological and medical contexts. In this paper, a Bayesian CART model is described and applied to the problem of modelling the cryptosporidiosis infection in Queensland, Australia. METHODOLOGY/PRINCIPAL FINDINGS: We compared the results of a Bayesian CART model with those obtained using a Bayesian spatial conditional autoregressive (CAR) model. Overall, the analyses indicated that the nature and magnitude of the effect estimates were similar for the two methods in this study, but the CART model more easily accommodated higher order interaction effects. CONCLUSIONS/SIGNIFICANCE: A Bayesian CART model for identification and estimation of the spatial distribution of disease risk is useful in monitoring and assessment of infectious diseases prevention and control.

Elicitation by design in ecology: using expert opinion to inform priors for Bayesian statistical models.

Bayesian statistical modeling has several benefits within an ecological context. In particular, when observed data are limited in sample size or representativeness, then the Bayesian framework provides a mechanism to combine observed data with other "prior" information. Prior information may be obtained from earlier studies, or in their absence, from expert knowledge. This use of the Bayesian framework reflects the scientific "learning cycle," where prior or initial estimates are updated when new data become available. In this paper we outline a framework for statistical design of expert elicitation processes for quantifying such expert knowledge, in a form suitable for input as prior information into Bayesian models. We identify six key elements: determining the purpose and motivation for using prior information; specifying the relevant expert knowledge available; formulating the statistical model; designing effective and efficient numerical encoding; managing uncertainty; and designing a practical elicitation protocol. We demonstrate this framework applies to a variety of situations, with two examples from the ecological literature and three from our experience. Analysis of these examples reveals several recurring important issues affecting practical design of elicitation in ecological problems.

Zero tolerance ecology: improving ecological inference by modelling the source of zero observations.

A common feature of ecological data sets is their tendency to contain many zero values. Statistical inference based on such data are likely to be inefficient or wrong unless careful thought is given to how these zeros arose and how best to model them. In this paper, we propose a framework for understanding how zero-inflated data sets originate and deciding how best to model them. We define and classify the different kinds of zeros that occur in ecological data and describe how they arise: either from 'true zero' or 'false zero' observations. After reviewing recent developments in modelling zero-inflated data sets, we use practical examples to demonstrate how failing to account for the source of zero inflation can reduce our ability to detect relationships in ecological data and at worst lead to incorrect inference. The adoption of methods that explicitly model the sources of zero observations will sharpen insights and improve the robustness of ecological analyses.