Development of Patient and Caregiver Conceptual Models Investigating the Health-Related Quality of Life Impacts of Malignant Pleural Mesothelioma.

BACKGROUND: Malignant pleural mesothelioma (MPM) is a rare and usually fatal malignancy frequently linked to occupational asbestos exposures and associated with poor prognosis and considerable humanistic burden. The study aimed to develop conceptual models of the health-related quality of life (HRQoL) impact on patients with and receiving treatment for MPM, and the burden on their caregivers. METHODS: This multi-country study (Australia and United Kingdom) adopted a qualitative methodology to conduct semi-structured, independent interviews with people with MPM (n = 26), current caregivers (n = 20), and caregivers of people who had recently died because of MPM (n = 4). Participants were recruited using a purposive sampling approach and interviews conducted via telephone between January 2021 and January 2022. Transcripts were analysed using thematic analysis and used to construct conceptual models. RESULTS: Patient analysis yielded four overarching themes: (1) debilitating burden of breathlessness and fatigue; (2) physical mesothelioma symptoms experienced by patients; (3) distress of MPM on the self and family; and (4) treatment is worth 'having a go' despite the potential impact on symptoms. Caregiver analysis yielded five core themes: (1) daily life limited by caregiving duties; (2) emotional well-being and the need for support; (3) the relational role shift to caregiver; (4) time spent providing care negatively impacts work and productivity; and (5) positive aspects and outcomes of caregiving. CONCLUSIONS: This study highlights the substantial daily and emotional HRQoL impact that MPM symptoms have on patients and caregivers. Both groups reduced work, productivity, and social and leisure activities. There was evidence of positive HRQoL impacts as a result of immunotherapy and radiotherapy, but less for chemotherapy. Caregiver impacts were intensified during the end-of-life period and persisted following patient death. Evident is a need for increased psychological support, information, and advice for caregivers, increased during the end-of-life period.

A Methodological Study to Compare Alternative Modes of Administration With Value EQ-5D Using Preference-Elicitation Techniques.

OBJECTIVES: Time trade-off (TTO) and discrete choice experiment (DCE) preference-elicitation techniques can be administered using face-to-face interviews (F2F), unassisted online (UO) surveys, or remote-assisted (RA) interviews. The objective of this study was to explore how the mode of administration affects the quality and reliability of preference-elicitation data. METHODS: EQ-5D-5L health states were valued using composite TTO (cTTO) and DCE approaches by the UK general population. Participants were allocated to 1 of 2 study groups. Group A completed both F2F and UO surveys (n = 271), and group B completed both RA and UO surveys (n = 223). The feasibility of survey completion and the reliability and face-validity of data collected were compared across all modes of administration. RESULTS: Fewer participants reported receiving sufficient guidance on the cTTO tasks during the UO survey compared with the 2 assisted modes. Participants across all modes typically reported receiving sufficient guidance on the DCE tasks. cTTO data were less reliable from the UO survey compared with both assisted modes, but there were no differences in DCE data reliability. cTTO data from all modes demonstrated face-validity; however, the UO survey produced higher utilities for moderate and severe health states than both assisted modes. Both F2F and RA modes provided comparably reliable data. CONCLUSIONS: The reliability of DCE data is not affected by the mode of administration. Interviewer-assisted modes of administration (F2F or RA) yield more reliable cTTO data than unassisted surveys. Both F2F and RA surveys produced similar-quality data.

Determination of output factor for CyberKnife using scintillation dosimetry and deep learning.

Objective. Small-field dosimetry is an ongoing challenge in radiotherapy quality assurance (QA) especially for radiosurgery systems such as CyberKnifeTM. The objective of this work is to demonstrate the use of a plastic scintillator imaged with a commercial camera to measure the output factor of a CyberKnife system. The output factor describes the dose on the central axis as a function of collimator size, and is a fundamental part of CyberKnife QA and integral to the data used in the treatment planning system.Approach. A self-contained device consisting of a solid plastic scintillator and a camera was build in a portable Pelicase. Photographs were analysed using classical methods and with convolutional neural networks (CNN) to predict beam parameters which were then compared to measurements.Main results. Initial results using classical image processing to determine standard QA parameters such as percentage depth dose (PDD) were unsuccessful, with 34% of points failing to meet the Gamma criterion (which measures the distance between corresponding points and the relative difference in dose) of 2 mm/2%. However, when images were processed using a CNN trained on simulated data and a green scintillator sheet, 92% of PDD curves agreed with measurements with a microdiamond detector to within 2 mm/2% and 78% to 1%/1 mm. The mean difference between the output factors measured using this system and a microdiamond detector was 1.1%. Confidence in the results was enhanced by using the algorithm to predict the known collimator sizes from the photographs which it was able to do with an accuracy of less than 1 mm.Significance. With refinement, a full output factor curve could be measured in less than an hour, offering a new approach for rapid, convenient small-field dosimetry.

Assessing the Content Validity of Preference-Based Measures in Cancer.

OBJECTIVES: This study assessed the content validity of generic and condition-specific preference-based measures (PBMs) with patients treated for cancer, evaluated against 10 Consensus-Based Standards for the Selection of Health Measurement Instruments criteria for good content validity, to best inform measurement strategies regarding the use of PBMs in oncology development programs and real-world applications. METHODS: Individual, semistructured interviews were conducted with patients who received drug treatment for cancer in the United Kingdom (n = 47) and the United States (n = 49). During the interview, patients completed 3 generic PBMs (EQ-5D-5L, EuroQol Health and Wellbeing measure-Short Form, Château Santé Base) and 2 condition-specific PBMs (Quality of Life Utility-Core 10 Dimension, Functional Assessment of Cancer Therapy Eight Dimension [FACT-8D]). Interviews were conducted via teleconference, audio recorded, and transcribed verbatim. Transcripts were coded using thematic and content analysis methods. RESULTS: Condition-specific measures were evaluated as having better relevancy than generic PBMs. Overall, the FACT-8D was evaluated as holding the best content validity in terms of relevancy, and the EuroQol Health and Wellbeing measure-Short Form received the most favorable evaluation of relevancy for generic PBMs. All measures demonstrated comparable comprehensiveness, with all suggested by patients to be missing concepts. The EQ-5D-5L was evaluated best in terms of comprehensibility. This was followed by the Quality of Life Utility-Core 10 Dimension and FACT-8D; both received similar evaluations. CONCLUSIONS: All measures were generally seen by patients as adequate in capturing appropriate aspects of health-related quality of life for measuring cancer outcomes, although together condition-specific measures were evaluated as having better relevancy than generic PBMs. Further health-related quality of life instrument development is encouraged, particularly with regard to the longer-term detrimental impacts of cancer and treatment side effects. Other developments could include new cancer-specific tools inclusive of conventional health items, treatment impacts, and psychological items.

Medical imaging applied to heritage.

The use of imaging has transformed the study of cultural heritage artefacts in the same way that medical imaging has transformed medicine. X-ray-based techniques are common in both medical and heritage imaging. Optical imaging, including scientific photography and spectral imaging techniques, is also common in both domains. Some common medical imaging methods such as ultrasound and MRI have not yet found routine application in heritage, whereas other methods such as imaging with charged and uncharged particles and 3D surface imaging are more common in heritage. Here, we review the field of heritage imaging from the point of view of medical imaging and include some classic challenges of heritage imaging such as reading the text on burnt scrolls, identifying underdrawings in paintings, and CT scanning of mummies, an ancient calculating device and sealed documents. We show how hyperspectral imaging can offer insight into the drawing techniques of Leonardo da Vinci and explain how laparoscopy has identified the method of construction of a 500-year-old pop-up anatomical text book.

The Rat Genome Database: Genetic, Genomic, and Phenotypic Data Across Multiple Species.

The laboratory rat, Rattus norvegicus, is an important model of human health and disease, and experimental findings in the rat have relevance to human physiology and disease. The Rat Genome Database (RGD, https://rgd.mcw.edu) is a model organism database that provides access to a wide variety of curated rat data including disease associations, phenotypes, pathways, molecular functions, biological processes, cellular components, and chemical interactions for genes, quantitative trait loci, and strains. We present an overview of the database followed by specific examples that can be used to gain experience in employing RGD to explore the wealth of functional data available for the rat and other species. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Navigating the Rat Genome Database (RGD) home page Basic Protocol 2: Using the RGD search functions Basic Protocol 3: Searching for quantitative trait loci Basic Protocol 4: Using the RGD genome browser (JBrowse) to find phenotypic annotations Basic Protocol 5: Using OntoMate to find gene-disease data Basic Protocol 6: Using MOET to find gene-ontology enrichment Basic Protocol 7: Using OLGA to generate gene lists for analysis Basic Protocol 8: Using the GA tool to analyze ontology annotations for genes Basic Protocol 9: Using the RGD InterViewer tool to find protein interaction data Basic Protocol 10: Using the RGD Variant Visualizer tool to find genetic variant data Basic Protocol 11: Using the RGD Disease Portals to find disease, phenotype, and other information Basic Protocol 12: Using the RGD Phenotypes & Models Portal to find qualitative and quantitative phenotype data and other rat strain-related information Basic Protocol 13: Using the RGD Pathway Portal to find disease and phenotype data via molecular pathways.

Rare disease research resources at the Rat Genome Database.

Rare diseases individually affect relatively few people, but as a group they impact considerable numbers of people. The Rat Genome Database (https://rgd.mcw.edu) is a knowledgebase that offers resources for rare disease research. This includes disease definitions, genes, quantitative trail loci (QTLs), genetic variants, annotations to published literature, links to external resources, and more. One important resource is identifying relevant cell lines and rat strains that serve as models for disease research. Diseases, genes, and strains have report pages with consolidated data, and links to analysis tools. Utilizing these globally accessible resources for rare disease research, potentiating discovery of mechanisms and new treatments, can point researchers toward solutions to alleviate the suffering of those afflicted with these diseases.

2022 updates to the Rat Genome Database: a Findable, Accessible, Interoperable, and Reusable (FAIR) resource.

The Rat Genome Database (RGD, https://rgd.mcw.edu) has evolved from simply a resource for rat genetic markers, maps, and genes, by adding multiple genomic data types and extensive disease and phenotype annotations and developing tools to effectively mine, analyze, and visualize the available data, to empower investigators in their hypothesis-driven research. Leveraging its robust and flexible infrastructure, RGD has added data for human and eight other model organisms (mouse, 13-lined ground squirrel, chinchilla, naked mole-rat, dog, pig, African green monkey/vervet, and bonobo) besides rat to enhance its translational aspect. This article presents an overview of the database with the most recent additions to RGD's genome, variant, and quantitative phenotype data. We also briefly introduce Virtual Comparative Map (VCMap), an updated tool that explores synteny between species as an improvement to RGD's suite of tools, followed by a discussion regarding the refinements to the existing PhenoMiner tool that assists researchers in finding and comparing quantitative data across rat strains. Collectively, RGD focuses on providing a continuously improving, consistent, and high-quality data resource for researchers while advancing data reproducibility and fulfilling Findable, Accessible, Interoperable, and Reusable (FAIR) data principles.

Mapping hippocampal glutamate in mesial temporal lobe epilepsy with glutamate weighted CEST (GluCEST) imaging.

Temporal lobe epilepsy (TLE) is one of the most common subtypes of focal epilepsy, with mesial temporal sclerosis (MTS) being a common radiological and histopathological finding. Accurate identification of MTS during presurgical evaluation confers an increased chance of good surgical outcome. Here we propose the use of glutamate-weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI) at 7 Tesla for mapping hippocampal glutamate distribution in epilepsy, allowing to differentiate lesional from non-lesional mesial TLE. We demonstrate that a directional asymmetry index, which quantifies the relative difference between GluCEST contrast in hippocampi ipsilateral and contralateral to the seizure onset zone, can differentiate between sclerotic and non-sclerotic hippocampi, even in instances where traditional presurgical MRI assessments did not provide evidence of sclerosis. Overall, our results suggest that hippocampal glutamate mapping through GluCEST imaging is a valuable addition to the presurgical epilepsy evaluation toolbox.

Ontological Analysis of Coronavirus Associated Human Genes at the COVID-19 Disease Portal.

The COVID-19 pandemic stemmed a parallel upsurge in the scientific literature about SARS-CoV-2 infection and its health burden. The Rat Genome Database (RGD) created a COVID-19 Disease Portal to leverage information from the scientific literature. In the COVID-19 Portal, gene-disease associations are established by manual curation of PubMed literature. The portal contains data for nine ontologies related to COVID-19, an embedded enrichment analysis tool, as well as links to a toolkit. Using these information and tools, we performed analyses on the curated COVID-19 disease genes. As expected, Disease Ontology enrichment analysis showed that the COVID-19 gene set is highly enriched with coronavirus infectious disease and related diseases. However, other less related diseases were also highly enriched, such as liver and rheumatic diseases. Using the comparison heatmap tool, we found nearly 60 percent of the COVID-19 genes were associated with nervous system disease and 40 percent were associated with gastrointestinal disease. Our analysis confirms the role of the immune system in COVID-19 pathogenesis as shown by substantial enrichment of immune system related Gene Ontology terms. The information in RGD's COVID-19 disease portal can generate new hypotheses to potentiate novel therapies and prevention of acute and long-term complications of COVID-19.

Technology readiness levels for machine learning systems.

The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, with mission critical measures and robustness throughout the process. Drawing on experience in both spacecraft engineering and machine learning (research through product across domain areas), we've developed a proven systems engineering approach for machine learning and artificial intelligence: the Machine Learning Technology Readiness Levels framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for machine learning workflows, including key distinctions from traditional software engineering, and a lingua franca for people across teams and organizations to work collaboratively on machine learning and artificial intelligence technologies. Here we describe the framework and elucidate with use-cases from physics research to computer vision apps to medical diagnostics.

Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI.

Accurate segmentation of surgical resection sites is critical for clinical assessments and neuroimaging research applications, including resection extent determination, predictive modeling of surgery outcome, and masking image processing near resection sites. In this study, an automated resection cavity segmentation algorithm is developed for analyzing postoperative MRI of epilepsy patients and deployed in an easy-to-use graphical user interface (GUI) that estimates remnant brain volumes, including postsurgical hippocampal remnant tissue. This retrospective study included postoperative T1-weighted MRI from 62 temporal lobe epilepsy (TLE) patients who underwent resective surgery. The resection site was manually segmented and reviewed by a neuroradiologist (JMS). A majority vote ensemble algorithm was used to segment surgical resections, using 3 U-Net convolutional neural networks trained on axial, coronal, and sagittal slices, respectively. The algorithm was trained using 5-fold cross validation, with data partitioned into training (N = 27) testing (N = 9), and validation (N = 9) sets, and evaluated on a separate held-out test set (N = 17). Algorithm performance was assessed using Dice-Sørensen coefficient (DSC), Hausdorff distance, and volume estimates. Additionally, we deploy a fully-automated, GUI-based pipeline that compares resection segmentations with preoperative imaging and reports estimates of resected brain structures. The cross-validation and held-out test median DSCs were 0.84 ± 0.08 and 0.74 ± 0.22 (median ± interquartile range) respectively, which approach inter-rater reliability between radiologists (0.84-0.86) as reported in the literature. Median 95 % Hausdorff distances were 3.6 mm and 4.0 mm respectively, indicating high segmentation boundary confidence. Automated and manual resection volume estimates were highly correlated for both cross-validation (r = 0.94, p < 0.0001) and held-out test subjects (r = 0.87, p < 0.0001). Automated and manual segmentations overlapped in all 62 subjects, indicating a low false negative rate. In control subjects (N = 40), the classifier segmented no voxels (N = 33), <50 voxels (N = 5), or a small volumes<0.5 cm3 (N = 2), indicating a low false positive rate that can be controlled via thresholding. There was strong agreement between postoperative hippocampal remnant volumes determined using automated and manual resection segmentations (r = 0.90, p < 0.0001, mean absolute error = 6.3 %), indicating that automated resection segmentations can permit quantification of postoperative brain volumes after epilepsy surgery. Applications include quantification of postoperative remnant brain volumes, correction of deformable registration, and localization of removed brain regions for network modeling.

MOET: a web-based gene set enrichment tool at the Rat Genome Database for multiontology and multispecies analyses.

Biological interpretation of a large amount of gene or protein data is complex. Ontology analysis tools are imperative in finding functional similarities through overrepresentation or enrichment of terms associated with the input gene or protein lists. However, most tools are limited by their ability to do ontology-specific and species-limited analyses. Furthermore, some enrichment tools are not updated frequently with recent information from databases, thus giving users inaccurate, outdated or uninformative data. Here, we present MOET or the Multi-Ontology Enrichment Tool (v.1 released in April 2019 and v.2 released in May 2021), an ontology analysis tool leveraging data that the Rat Genome Database (RGD) integrated from in-house expert curation and external databases including the National Center for Biotechnology Information (NCBI), Mouse Genome Informatics (MGI), The Kyoto Encyclopedia of Genes and Genomes (KEGG), The Gene Ontology Resource, UniProt-GOA, and others. Given a gene or protein list, MOET analysis identifies significantly overrepresented ontology terms using a hypergeometric test and provides nominal and Bonferroni corrected P-values and odds ratios for the overrepresented terms. The results are shown as a downloadable list of terms with and without Bonferroni correction, and a graph of the P-values and number of annotated genes for each term in the list. MOET can be accessed freely from https://rgd.mcw.edu/rgdweb/enrichment/start.html.

Cambrian edrioasteroid reveals new mechanism for secondary reduction of the skeleton in echinoderms.

Echinoderms are characterized by a distinctive high-magnesium calcite endoskeleton as adults, but elements of this have been drastically reduced in some groups. Herein, we describe a new pentaradial echinoderm, Yorkicystis haefneri n. gen. n. sp., which provides, to our knowledge, the oldest evidence of secondary non-mineralization of the echinoderm skeleton. This material was collected from the Cambrian Kinzers Formation in York (Pennsylvania, USA) and is dated as ca 510 Ma. Detailed morphological observations demonstrate that the ambulacra (i.e. axial region) are composed of flooring and cover plates, but the rest of the body (i.e. extraxial region) is preserved as a dark film and lacks any evidence of skeletal plating. Moreover, X-ray fluorescence analysis reveals that the axial region is elevated in iron. Based on our morphological and chemical data and on taphonomic comparisons with other fossils from the Kinzers Formation, we infer that the axial region was originally calcified, while the extraxial region was non-mineralized. Phylogenetic analyses recover Yorkicystis as an edrioasteroid, indicating that this partial absence of skeleton resulted from a secondary reduction. We hypothesize that skeletal reduction resulted from lack of expression of the skeletogenic gene regulatory network in the extraxial body wall during development. Secondary reduction of the skeleton in Yorkicystis might have allowed for greater flexibility of the body wall.

Farewell editorial.

Biomedical Optics Express Editor-in-Chief Christoph K. Hitzenberger and Deputy Editor Adam Gibson share some final remarks as they prepare to end their editorial terms on 31 December 2021.

Multi-Domain Touchscreen-Based Cognitive Assessment of C57BL/6J Female Mice Shows Whole-Body Exposure to 56Fe Particle Space Radiation in Maturity Improves Discrimination Learning Yet Impairs Stimulus-Response Rule-Based Habit Learning.

Astronauts during interplanetary missions will be exposed to galactic cosmic radiation, including charged particles like 56Fe. Most preclinical studies with mature, "astronaut-aged" rodents suggest space radiation diminishes performance in classical hippocampal- and prefrontal cortex-dependent tasks. However, a rodent cognitive touchscreen battery unexpectedly revealed 56Fe radiation improves the performance of C57BL/6J male mice in a hippocampal-dependent task (discrimination learning) without changing performance in a striatal-dependent task (rule-based learning). As there are conflicting results on whether the female rodent brain is preferentially injured by or resistant to charged particle exposure, and as the proportion of female vs. male astronauts is increasing, further study on how charged particles influence the touchscreen cognitive performance of female mice is warranted. We hypothesized that, similar to mature male mice, mature female C57BL/6J mice exposed to fractionated whole-body 56Fe irradiation (3 × 6.7cGy 56Fe over 5 days, 600 MeV/n) would improve performance vs. Sham conditions in touchscreen tasks relevant to hippocampal and prefrontal cortical function [e.g., location discrimination reversal (LDR) and extinction, respectively]. In LDR, 56Fe female mice more accurately discriminated two discrete conditioned stimuli relative to Sham mice, suggesting improved hippocampal function. However, 56Fe and Sham female mice acquired a new simple stimulus-response behavior and extinguished this acquired behavior at similar rates, suggesting similar prefrontal cortical function. Based on prior work on multiple memory systems, we next tested whether improved hippocampal-dependent function (discrimination learning) came at the expense of striatal stimulus-response rule-based habit learning (visuomotor conditional learning). Interestingly, 56Fe female mice took more days to reach criteria in this striatal-dependent rule-based test relative to Sham mice. Together, our data support the idea of competition between memory systems, as an 56Fe-induced decrease in striatal-based learning is associated with enhanced hippocampal-based learning. These data emphasize the power of using a touchscreen-based battery to advance our understanding of the effects of space radiation on mission critical cognitive function in females, and underscore the importance of preclinical space radiation risk studies measuring multiple cognitive processes, thereby preventing NASA's risk assessments from being based on a single cognitive domain.

COVID-19 pandemic impacts on conservation research, management, and public engagement in US national parks.

The COVID-19 pandemic has disrupted the timing and substance of conservation research, management, and public engagement in protected areas around the world. This disruption is evident in US national parks, which play a key role in protecting natural and cultural resources and providing outdoor experiences for the public. Collectively, US national parks protect 34 million ha, host more than 300 million visits annually, and serve as one of the world's largest informal education organizations. The pandemic has altered park conditions and operations in a variety of ways. Shifts in operational conditions related to safety issues, reduced staffing, and decreased park revenues have forced managers to make difficult trade-offs among competing priorities. Long-term research and monitoring of the health of ecosystems and wildlife populations have been interrupted. Time-sensitive management practices, such as control of invasive plants and restoration of degraded habitat, have been delayed. And public engagement has largely shifted from in-person experiences to virtual engagement through social media and other online interactions. These changes pose challenges for accomplishing important science, management, and public engagement goals, but they also create opportunities for developing more flexible monitoring programs and inclusive methods of public engagement. The COVID-19 pandemic reinforces the need for strategic science, management planning, flexible operations, and online public engagement to help managers address rapid and unpredictable challenges.

Health state utilities associated with caring for an individual with cutaneous T-cell lymphoma (CTCL).

AIM: Cutaneous T-cell Lymphoma (CTCL) is a rare form of non-Hodgkin lymphoma characterized by skin lesions, which can negatively impact the quality of life of both patients and their caregivers. The Decision Support Unit (DSU) at the National Institute for Health and Care Excellence (NICE) in the UK recently outlined a rationale for the inclusion of caregiver burden in economic evaluations. This study aimed to estimate utilities for health states associated with being a caregiver for an individual with CTCL at different stages of treatment. MATERIALS AND METHODS: A targeted literature review and interviews with CTCL specialists informed the development of health state vignettes describing the experience caring for an individual with CTCL. The vignettes were evaluated in interviews with the UK general population using a visual analogue scale (VAS), the time trade-off (TTO) method and the EQ-5D-5L. RESULTS: Four vignettes were developed describing the caregiver experience for an individual with CTCL on i) second line treatment, ii) third line treatment, iii) end of life care, iv) a post-patient death. One hundred interviews were conducted to evaluate the health state vignettes. The pattern of results was similar across the evaluation methods: second line treatment (VAS: 39.2, TTO = 0.52, EQ-5D-5L: 0.56), third line treatment (VAS: 31.1, TTO = 0.39, EQ-5D-5L: 0.37), end of life care (VAS: 28.2, TTO = 0.37, EQ-5D-5L: 0.31) and post-patient death (VAS: 41.2, TTO = 0.63, EQ-5D-5L: 0.59). Limitations and conclusions: These findings highlight the substantial burden of caring for an individual with CTCL and the importance of including caregiver burden in the health technology assessment review process. A limitation is the hypothetical vignette approach, which meant the TTO participants did not have experience of caring for individuals with CTCL, but were imagining this state. There is also the possibility that they may also be considering the patient experience when responding to the questions.

Clients' experiences of shared decision making in an integrative psychotherapy for depression.

Mental health and general health care research has shown that practitioners can facilitate patient involvement in shared decision making (SDM) and that the approach can benefit patients who wish to take part in decisions around their care. Yet patient experiences of SDM within a psychotherapy context have been little researched. This study examined how clients experienced SDM in a collaborative-integrative psychotherapy. A grounded theory approach used interpersonal process recall interviewing and supplementary semi-structured interviews to investigate 14 clients' experiences of SDM in pluralistic psychotherapy for depression. Verbatim transcripts were coded into 819 meaning units across six categories containing 13 subcomponents that comprised a single, core category. The six categories were (a) experiencing decisions as shared, (b) psychotherapists supporting clients to become more active in the decision-making process, (c) both parties presenting and recognizing expert knowledge, (d) clients felt recognized as an individual and accommodated for by their psychotherapist, (e) clients felt comfortable engaging with the decision-making process, and (f) daunting for clients to be asked to take part in decision discussions. A core category emerged of "Psychotherapists encourage client participation and progressively support clients to provide information and contributions towards shared treatment decisions that could be led equally, or marginally more by one party." Such support was particularly useful when clients had difficulty contributing as part of decision discussions. Client preferences for SDM change across clients and across decisions, highlighting the importance of practitioners remaining flexible to individual clients when using the approach.

Technical Note: Simulation of dose buildup in proton pencil beams.

PURPOSE: The purpose of this study is to characterize the magnitude and depth of dose buildup in pencil beam scanning proton therapy. METHODS: We simulate the integrated depth-dose curve of realistic proton pencil beams in a water phantom using the Geant4 Monte Carlo toolkit. We independently characterize the electronic and protonic components of dose buildup as a function of proton beam energy from 40 to 400 MeV, both with and without an air gap. RESULTS: At clinical energies, electronic buildup over a distance of about 1 mm leads to a dose reduction at depth of the basal layer (0.07 mm) by up to 6% compared to if no buildup effect were present. Protonic buildup reduces the dose to the basal layer by up to 16% and has effects at depths of up to 150 mm. Secondary particles with a mass number A > 1 do not contribute to dose buildup. An air gap of 1 m has no significant effect on protonic buildup but reduces electronic buildup below 1%. CONCLUSIONS: Protonic and electronic dose buildup are relevant for accurate dosimetry in proton therapy although a realistic air gap reduces the electronic buildup to levels where it can be safely neglected. We recommend including electrons and secondary protons in Monte Carlo-based treatment planning systems down to a predicted range of 10-20 µ m in order to accurately model the dose at depths of the basal layer, no matter the size of the air gap between nozzle and patient.