With many thyroid nodules being incidentally detected, it is important to identify as many malignant nodules as possible while excluding those that are highly likely to be benign from fine needle aspiration (FNA) biopsies or surgeries. This paper presents a computer-aided diagnosis (CAD) system for classifying thyroid nodules in ultrasound images. We use deep learning approach to extract features from thyroid ultrasound images. Ultrasound images are pre-processed to calibrate their scale and remove the artifacts. A pre-trained GoogLeNet model is then fine-tuned using the pre-processed image samples which leads to superior feature extraction. The extracted features of the thyroid ultrasound images are sent to a Cost-sensitive Random Forest classifier to classify the images into “malignant” and “benign” cases. The experimental results show the proposed fine-tuned GoogLeNet model achieves excellent classification performance, attaining 98.29% classification accuracy, 99.10% sensitivity and 93.90% specificity for the images in an open access database (Pedraza et al. 16), while 96.34% classification accuracy, 86% sensitivity and 99% specificity for the images in our local health region database.

Clinical pathways (CPWs) are a common component in the quest to improve the quality of health. CPWs are used to reduce variation, improve quality of care, and maximize the outcomes for specific groups of patients. An ongoing challenge is the operationalization of a definition of CPW in healthcare. This may be attributable to both the differences in definition and a lack of conceptualization in the field of clinical pathways. This correspondence article describes a process of refinement of an operational definition for CPW research and proposes an operational definition for the future syntheses of CPWs literature. Following the approach proposed by Kinsman et al. (BMC Medicine 8(1):31, 2010) and Wieland et al. (Alternative Therapies in Health and Medicine 17(2):50, 2011), we used a four-stage process to generate a five criteria checklist for the definition of CPWs. We refined the operational definition, through consensus, merging two of the checklist’s criteria, leading to a more inclusive criterion for accommodating CPW studies conducted in various healthcare settings. The following four criteria for CPW operational definition, derived from the refinement process described above, are (1) the intervention was a structured multidisciplinary plan of care; (2) the intervention was used to translate guidelines or evidence into local structures; (3) the intervention detailed the steps in a course of treatment or care in a plan, pathway, algorithm, guideline, protocol or other ‘inventory of actions’ (i.e. the intervention had time-frames or criteria-based progression); and (4) the intervention aimed to standardize care for a specific population. An intervention meeting all four criteria was considered to be a CPW. The development of operational definitions for complex interventions is a useful approach to appraise and synthesize evidence for policy development and quality improvement.

We have performed a narrative synthesis. A literature search was conducted between January 2000 and June 2014 in 7 databases. The initial search identified 2717 articles; 319 underwent abstract screening, 67 underwent full-text screening, and 25 final articles were included. This review looked at early stage breast cancer in women only, excluding ductal carcinoma in situ and advanced breast cancer. A conceptual framework was created to organize the central constructs underlying women's choices: clinicopathologic factors, physician factors, and individual factors with subgroups of sociodemographic, geographic, and personal beliefs and preferences. This framework guided our review's synthesis and analysis. We found that larger tumor size and increasing stage was associated with increased rates of mastectomy. The results for age varied, but suggested that old and young extremes of diagnostic age were associated with an increased likelihood of mastectomy. Higher socioeconomic status was associated with higher breast conservation therapy (BCT) rates. Resident rural location and increasing distance from radiation treatment facilities were associated with lower rates of BCT. Individual belief factors influencing women's choice of mastectomy (mastectomy being reassuring, avoiding radiation, an expedient treatment) differed from factors influencing choice of BCT (body image and femininity, physician recommendation, survival equivalence, less surgery). Surgeon factors, including female gender, higher case numbers, and individual surgeon practice, were associated with increased BCT rates. The decision-making process for women with early stage breast cancer is complicated and affected by multiple factors. Organizing these factors into central constructs of clinicopathologic, individual, and physician factors may aid health-care professionals to better understand this process.

Background: Shared Decision-making (SDM), a medical decision-making model, was popularized in the late 1980s in reaction to then predominate paternalistic decision-making, aiming to better meet the needs of patients. Extensive research has been conducted internationally examining the benefits of SDM implementation; however, existing theory on how SDM works, for whom, in which circumstances, and why is limited. While literature has shown positive patient, health care provider, and system benefits (SDM outputs), further research is required to understand the nuances of this type of decision-making. As such, we set out to address: “In which situations, how, why, and for whom does SDM between patients and health care providers contribute to improved engagement in the Shared Decision-making process?” Methods: To achieve our study goals we conducted a seven-step realist synthesis process, which included: (1) preliminary program theory development, (2) search strategy development, (3) selection and appraisal of literature in accordance with realist methodology, (4) data extraction, (5) identification of relevant formal theories, (6) data analysis and synthesis, and (7) formation of a revised program theory with the input of stakeholders. This process was done in accordance with RAMESES guidelines and publication standards for a realist synthesis. Expert consultations were also held to ensure consistency within the SDM literature. Results: Through our realist synthesis, we developed a program theory of SDM which includes three contexts (pre-existing relationship, difficulty of decision, and system support), eight mechanism sets (anxiety, trust, perception of other party capacity, perception of time, self-efficacy, world view, perception of capacity to external support, and recognition of decision), and one outcome (engagement in SDM). Conclusions: As far as the authors of this paper are aware, this paper is the first to begin unpacking how SDM works, for whom, in which circumstances, and why. By examining key mechanism sets and exploring how they facilitate or inhibit SDM, we have produced a program theory that may assist health care professionals, policy makers, and patients. While further research is suggested to further unpack the concepts identified within this paper, this provides an initial understanding into the theory behind SDM. Registration: PROSPERO: CRD42017062609.

In January 2012 an acute care surgery (ACS) model was introduced at St. Paul’s Hospital, Saskatoon, Saskatchewan. The goal of implementing an ACS service was to improve the delivery of care for emergent, non-trauma surgical patients. We examined whether the ACS model improved wait time to surgery, decreased the proportion of surgeries performed after hours, and shortened post-surgical length of stay. We also assessed whether the surgeons working in an ACS system had higher on-call satisfaction than surgeons working in a non- ACS system.