Project description:ObjectivesOpen-access cancer imaging datasets have become integral for evaluating novel AI approaches in radiology. However, their use in quantitative analysis with radiomics features presents unique challenges, such as incomplete documentation, low visibility, non-uniform data formats, data inhomogeneity, and complex preprocessing. These issues may cause problems with reproducibility and standardization in radiomics studies.MethodsWe systematically reviewed imaging datasets with public copyright licenses, published up to March 2023 across four large online cancer imaging archives. We included only datasets with tomographic images (CT, MRI, or PET), segmentations, and clinical annotations, specifically identifying those suitable for radiomics research. Reproducible preprocessing and feature extraction were performed for each dataset to enable their easy reuse.ResultsWe discovered 29 datasets with corresponding segmentations and labels in the form of health outcomes, tumor pathology, staging, imaging-based scores, genetic markers, or repeated imaging. We compiled a repository encompassing 10,354 patients and 49,515 scans. Of the 29 datasets, 15 were licensed under Creative Commons licenses, allowing both non-commercial and commercial usage and redistribution, while others featured custom or restricted licenses. Studies spanned from the early 1990s to 2021, with the majority concluding after 2013. Seven different formats were used for the imaging data. Preprocessing and feature extraction were successfully performed for each dataset.ConclusionRadiomicsHub is a comprehensive public repository with radiomics features derived from a systematic review of public cancer imaging datasets. By converting all datasets to a standardized format and ensuring reproducible and traceable processing, RadiomicsHub addresses key reproducibility and standardization challenges in radiomics.Critical relevance statementThis study critically addresses the challenges associated with locating, preprocessing, and extracting quantitative features from open-access datasets, to facilitate more robust and reliable evaluations of radiomics models.Key points- Through a systematic review, we identified 29 cancer imaging datasets suitable for radiomics research. - A public repository with collection overview and radiomics features, encompassing 10,354 patients and 49,515 scans, was compiled. - Most datasets can be shared, used, and built upon freely under a Creative Commons license. - All 29 identified datasets have been converted into a common format to enable reproducible radiomics feature extraction.

Project description:BackgroundPharmacy interventions are a subset of public health interventions and its research is usually performed within the scope of a trial. The economic evaluation of pharmacy interventions requires certain considerations which have some similarities to those of public health interventions and to economic evaluations alongside trials. The objective of this research is to perform an overview of systematic reviews of economic evaluations of pharmacy services and triangulate results with recommendations for economic evaluations of both public health interventions and alongside trials.Methods(1) Exploratory review of recommendations on the economic evaluation of public health interventions, (2) exploratory review of recommendations for conducting economic evaluations alongside trials, (3) overview of systematic reviews of economic evaluations of pharmacy interventions (protocol registered with PROSPERO 2016 outlining information sources, inclusion criteria, appraisal of reviews and synthesis methods).ResultsFourteen systematic reviews containing 75 index publications were included. Reviews reported favorable economic findings for 71% of studies with full economic evaluations. The types of economic analysis are diverse. Two critical quality domains are absent from most reviews. Key findings include the following: certain types of risk of bias, wider scope of study designs, and most economic quality criteria met but some issues unresolved or unclear. Triangulation revealed additional gaps. Limitations include choice of critical quality domains and potential biases in the overview process.ConclusionsEconomic evaluations of pharmacy-based public health interventions seem to follow most economic quality criteria, but there are still some issues in certain key areas to improve. These findings may assist in improving the design of pilot trials of economic evaluations in pharmacy, leading to robust evidence for payers. Based on the findings, we propose a methodological approach for the economic evaluation of pharmacy-based public health interventions.Systematic review registrationPROSPERO CRD42016032768.

Project description:Decision makers increasingly require cost-effectiveness evidence to inform resource allocation and the need for systematic reviews of economic evaluations (SREEs) has grown accordingly. The objective of this article is to describe current practice and identify unique challenges in conducting and reporting SREEs. Current guideline documents for SREEs were consulted and summarised. A rapid review of English-language SREEs, using MEDLINE and EMBASE, published in 2017/2018, containing at least 20 studies was undertaken to describe current practice. Information on data extraction methods, quality assessment (QA) tools and reporting methods were narratively summarised. Lessons learned from a recently conducted SREE of weight loss interventions for severely obese adults were discussed. Sixty-three publications were included in the rapid review. Substantial heterogeneity in review methods, reporting standards and QA approaches was evident. Our recently conducted SREE on weight loss interventions identified scope to improve process efficiency, opportunity for more transparent and succinct reporting, and potential to improve consistency of QA. Practical solutions may include (1) using pre-piloted data extraction forms linked explicitly to results tables; (2) consistently reporting on key assumptions and sensitivity analyses that drive results; and (3) using checklists that include topic-specific items where relevant and allow reviewers to distinguish between reporting, justification and QA. The lack of a mutually agreed, standardised set of best practice guidelines has led to substantial heterogeneity in the conduct and reporting of SREEs. Future work is required to standardise the approach to conducting SREEs so that they can generate efficient, timely and relevant evidence to support decision-making. We suggest only data extracting information that will be reported, focusing discussion around the key drivers of cost-effectiveness, and improving consistency in QA by distinguishing between what is reported, justified by authors and deemed appropriate by the reviewer.

Project description:Despite the accumulation of data and studies, deciphering animal vocal communication remains challenging. In most cases, researchers must deal with the sparse recordings composing Small, Unbalanced, Noisy, but Genuine (SUNG) datasets. SUNG datasets are characterized by a limited number of recordings, most often noisy, and unbalanced in number between the individuals or categories of vocalizations. SUNG datasets therefore offer a valuable but inevitably distorted vision of communication systems. Adopting the best practices in their analysis is essential to effectively extract the available information and draw reliable conclusions. Here we show that the most recent advances in machine learning applied to a SUNG dataset succeed in unraveling the complex vocal repertoire of the bonobo, and we propose a workflow that can be effective with other animal species. We implement acoustic parameterization in three feature spaces and run a Supervised Uniform Manifold Approximation and Projection (S-UMAP) to evaluate how call types and individual signatures cluster in the bonobo acoustic space. We then implement three classification algorithms (Support Vector Machine, xgboost, neural networks) and their combination to explore the structure and variability of bonobo calls, as well as the robustness of the individual signature they encode. We underscore how classification performance is affected by the feature set and identify the most informative features. In addition, we highlight the need to address data leakage in the evaluation of classification performance to avoid misleading interpretations. Our results lead to identifying several practical approaches that are generalizable to any other animal communication system. To improve the reliability and replicability of vocal communication studies with SUNG datasets, we thus recommend: i) comparing several acoustic parameterizations; ii) visualizing the dataset with supervised UMAP to examine the species acoustic space; iii) adopting Support Vector Machines as the baseline classification approach; iv) explicitly evaluating data leakage and possibly implementing a mitigation strategy.

Project description:Reconstructing the dynamics of populations is complicated by the different types of stochasticity experienced by populations, in particular if some forms of stochasticity introduce bias in parameter estimation in addition to error. Identification of systematic biases is critical when determining whether the intrinsic dynamics of populations are stable or unstable and whether or not populations exhibit an Allee effect, i.e., a minimum size below which deterministic extinction should follow. Using a simulation model that allows for Allee effects and a range of intrinsic dynamics, we investigated how three types of stochasticity--demographic, environmental, and random catastrophes--affect our ability to reconstruct the intrinsic dynamics of populations. Demographic stochasticity aside, which is only problematic in small populations, we find that environmental stochasticity--positive and negative environmental fluctuations--caused increased error in parameter estimation, but bias was rarely problematic, except at the highest levels of noise. Random catastrophes, events causing large-scale mortality and likely to be more common than usually recognized, caused immediate bias in parameter estimates, in particular when Allee effects were large. In the latter case, population stability was predicted when endogenous dynamics were actually unstable and the minimum viable population size was overestimated in populations with small or non-existent Allee effects. Catastrophes also generally increased extinction risk, in particular when endogenous Allee effects were large. We propose a method for identifying data points likely resulting from catastrophic events when such events have not been recorded. Using social spider colonies (Anelosimus spp.) as models for populations, we show that after known or suspected catastrophes are accounted for, reconstructed growth parameters are consistent with intrinsic dynamical instability and substantial Allee effects. Our results are applicable to metapopulation or time series data and are relevant for predicting extinction in conservation applications or the management of invasive species.

Project description:PURPOSE:To systematically evaluate and summarize evidence across multiple systematic reviews (SRs) examining interventions addressing polypharmacy. SUMMARY:MEDLINE, the Cochrane Database of Systematic Reviews, and the Database of Abstracts of Reviews of Effects (DARE) were searched for SRs evaluating interventions addressing polypharmacy in adults published from January 2004 to February 2017. Two authors independently screened, appraised, and extracted information. SRs with Assessment of Multiple Systematic Reviews (AMSTAR) scores below 8 were excluded. After extraction of relevant conclusions from each SR, evidence was summarized and conclusions compared. Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was used to assess evidence quality. Six SRs met the inclusion criteria, 4 of which used meta-analytic pooling. Five SRs focused on older adults. Four were not restricted to any specific disease type, whereas 1 focused on proton pump inhibitors and another focused on patients with severe dementia. Care settings and measured outcomes varied widely. SRs examining the impact on patient-centered outcomes, including morbidity, mortality, patient satisfaction, and utilization, found inconsistent evidence regarding the benefit of polypharmacy interventions, but most concluded that interventions had either null or uncertain impact. Two SRs assessing medication appropriateness found very low-quality evidence of modest improvements with polypharmacy interventions. CONCLUSION:An overview of SRs of interventions to address polypharmacy found 6 recent and high-quality SRs, mostly focused on older adults, in which both process and outcome measures were used to evaluate interventions. Despite the low quality of evidence in the underlying primary studies, both SRs that assessed medication appropriateness found evidence that polypharmacy interventions improved it. However, there was no consistent evidence of any impact on downstream patient-centered outcomes such as healthcare utilization, morbidity, or mortality.

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Addressing the challenges of reconstructing systematic reviews datasets: a case study and a noisy label filter procedure

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