Knee'd to Know Basis: Informed Consent in Total Hip and Knee Arthroplasty.

Informed consent is the process by which a medical provider explains the benefits, risks, and alternatives to a proposed medical intervention. It is a crucial part of maintaining patient autonomy and is particularly important in the context of elective surgical procedures, such as joint arthroplasty. The goal of this article is to review the topic of informed consent in the context of total joint arthroplasty. In this review, we discuss informed consent in general, considerations for informed consent in general arthroplasty procedures, and special 12 considerations for both hip and knee arthroplasty.

Machine-learning based investigation of prognostic indicators for oncological outcome of pancreatic ductal adenocarcinoma.

Introduction: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a poor prognosis. Surgical resection remains the only potential curative treatment option for early-stage resectable PDAC. Patients with locally advanced or micrometastatic disease should ideally undergo neoadjuvant therapy prior to surgical resection for an optimal treatment outcome. Computerized tomography (CT) scan is the most common imaging modality obtained prior to surgery. However, the ability of CT scans to assess the nodal status and resectability remains suboptimal and depends heavily on physician experience. Improved preoperative radiographic tumor staging with the prediction of postoperative margin and the lymph node status could have important implications in treatment sequencing. This paper proposes a novel machine learning predictive model, utilizing a three-dimensional convoluted neural network (3D-CNN), to reliably predict the presence of lymph node metastasis and the postoperative positive margin status based on preoperative CT scans. Methods: A total of 881 CT scans were obtained from 110 patients with PDAC. Patients and images were separated into training and validation groups for both lymph node and margin prediction studies. Per-scan analysis and per-patient analysis (utilizing majority voting method) were performed. Results: For a lymph node prediction 3D-CNN model, accuracy was 90% for per-patient analysis and 75% for per-scan analysis. For a postoperative margin prediction 3D-CNN model, accuracy was 81% for per-patient analysis and 76% for per-scan analysis. Discussion: This paper provides a proof of concept that utilizing radiomics and the 3D-CNN deep learning framework may be used preoperatively to improve the prediction of positive resection margins as well as the presence of lymph node metastatic disease. Further investigations should be performed with larger cohorts to increase the generalizability of this model; however, there is a great promise in the use of convoluted neural networks to assist clinicians with treatment selection for patients with PDAC.

Prevaccine Human Papillomavirus Status in Invasive and Intraepithelial Lesions of the Vulva in New Zealand Women.

OBJECTIVE: The human papillomavirus (HPV) vaccine, introduced in New Zealand (NZ) in 2008, is predicted to substantially lower the incidence of HPV-associated precancers and cancers. The aim of this study is to estimate the proportion of vulvar intraepithelial neoplasia (VIN) lesions and invasive vulvar squamous cell carcinomas (SCCV) attributable to HPV in NZ women treated by the Auckland Regional Gynecological Oncology Service, covering an estimated 50% of the NZ population. MATERIALS AND METHODS: Polymerase chain reaction and reverse hybridization were used to analyze retrospective histologically proven SCCV from 1990 to 2007 and VIN lesions from 2000 to 2007 for HPV content and genotype in a collaborative study with the Catalan Institute of Oncology. Immunohistochemistry for p16INK4a was performed on SCCV, which were attributed to HPV if both tested positive. RESULTS: Polymerase chain reaction testing for HPV content and genotype was performed on 66 VIN lesions (all high-grade squamous intraepithelial lesions) and 189 SCCV. In addition, p16 immunohistochemistry was performed on 168 of the 189 SCCV (88.9%) tested for HPV-DNA. Overall, 61 SCCV cases (36.3%) were attributed to HPV (HPV+/p16+), and 89 SCCV cases (53%) were considered to have developed independently of HPV (HPV-/p16-). Known high-risk HPV genotypes were present in 96.8% of HPV-DNA-positive vulvar high-grade squamous intraepithelial lesions and 98.4% of HPV-attributable SCCV. Human papillomavirus 16 represented the most common genotype in both. CONCLUSIONS: Overall, the HPV vaccine is likely to substantially alter the profile of SCCV in our region. The results provide a baseline assessment of the HPV status of vulvar neoplasia before the introduction of the HPV vaccine.

autopsych: An R Shiny tool for the reproducible Rasch analysis, differential item functioning, equating, and examination of group effects.

In this paper, we present autopsych, a novel online tool that allows school assessment experts, test developers, and researchers to perform routine psychometric analyses and equating of student test data and to examine the effect of student demographic and group conditions on student test performance. The app extends current open-source software by providing (1) extensive embedded result narration and summaries for written reports, (2) improved handling of partial credit data via customizable item-person Wright maps, (3) customizable item- and person-flagging systems, (4) item-response theory model constraints and controls, (5) many-facets Rasch analysis to examine item bias, (6) Rasch fixed item equating for mapping student ability across test forms, (7) tabbed spreadsheet outputs and immediate options for secondary data analysis, (8) customizable graphical color schemes, (9) extended ANOVA analysis for examining group differences, and (10) inter-rater reliability analyses for the verifying the consistency of rater scoring systems. We present the app's architecture and functionalities and test its performance with simulated and real-world small-, medium-, and large-scale assessment data. Implications and planned future developments are also discussed.

Authenticity and credibility aware detection of adverse drug events from social media.

OBJECTIVES: Adverse drug events (ADEs) are among the top causes of hospitalization and death. Social media is a promising open data source for the timely detection of potential ADEs. In this paper, we study the problem of detecting signals of ADEs from social media. METHODS: Detecting ADEs whose drug and AE may be reported in different posts of a user leads to major concerns regarding the content authenticity and user credibility, which have not been addressed in previous studies. Content authenticity concerns whether a post mentions drugs or adverse events that are actually consumed or experienced by the writer. User credibility indicates the degree to which chronological evidence from a user's sequence of posts should be trusted in the ADE detection. We propose AC-SPASM, a Bayesian model for the authenticity and credibility aware detection of ADEs from social media. The model exploits the interaction between content authenticity, user credibility and ADE signal quality. In particular, we argue that the credibility of a user correlates with the user's consistency in reporting authentic content. RESULTS: We conduct experiments on a real-world Twitter dataset containing 1.2 million posts from 13,178 users. Our benchmark set contains 22 drugs and 8089 AEs. AC-SPASM recognizes authentic posts with F1 - the harmonic mean of precision and recall of 80%, and estimates user credibility with precision@10 = 90% and NDCG@10 - a measure for top-10 ranking quality of 96%. Upon validation against known ADEs, AC-SPASM achieves F1 = 91%, outperforming state-of-the-art baseline models by 32% (p < 0.05). Also, AC-SPASM obtains precision@456 = 73% and NDCG@456 = 94% in detecting and prioritizing unknown potential ADE signals for further investigation. Furthermore, the results show that AC-SPASM is scalable to large datasets. CONCLUSIONS: Our study demonstrates that taking into account the content authenticity and user credibility improves the detection of ADEs from social media. Our work generates hypotheses to reduce experts' guesswork in identifying unknown potential ADEs.

Authenticity and credibility aware detection of adverse drug events from social media.

OBJECTIVES: Adverse drug events (ADEs) are among the top causes of hospitalization and death. Social media is a promising open data source for the timely detection of potential ADEs. In this paper, we study the problem of detecting signals of ADEs from social media. METHODS: Detecting ADEs whose drug and AE may be reported in different posts of a user leads to major concerns regarding the content authenticity and user credibility, which have not been addressed in previous studies. Content authenticity concerns whether a post mentions drugs or adverse events that are actually consumed or experienced by the writer. User credibility indicates the degree to which chronological evidence from a user's sequence of posts should be trusted in the ADE detection. We propose AC-SPASM, a Bayesian model for the authenticity and credibility aware detection of ADEs from social media. The model exploits the interaction between content authenticity, user credibility and ADE signal quality. In particular, we argue that the credibility of a user correlates with the user's consistency in reporting authentic content. RESULTS: We conduct experiments on a real-world Twitter dataset containing 1.2 million posts from 13,178 users. Our benchmark set contains 22 drugs and 8089 AEs. AC-SPASM recognizes authentic posts with F1 - the harmonic mean of precision and recall of 80%, and estimates user credibility with precision@10 = 90% and NDCG@10 - a measure for top-10 ranking quality of 96%. Upon validation against known ADEs, AC-SPASM achieves F1 = 91%, outperforming state-of-the-art baseline models by 32% (p < 0.05). Also, AC-SPASM obtains precision@456 = 73% and NDCG@456 = 94% in detecting and prioritizing unknown potential ADE signals for further investigation. Furthermore, the results show that AC-SPASM is scalable to large datasets. CONCLUSIONS: Our study demonstrates that taking into account the content authenticity and user credibility improves the detection of ADEs from social media. Our work generates hypotheses to reduce experts' guesswork in identifying unknown potential ADEs.

Detecting signals of detrimental prescribing cascades from social media.

MOTIVATION: Prescribing cascade (PC) occurs when an adverse drug reaction (ADR) is misinterpreted as a new medical condition, leading to further prescriptions for treatment. Additional prescriptions, however, may worsen the existing condition or introduce additional adverse effects (AEs). Timely detection and prevention of detrimental PCs is essential as drug AEs are among the leading causes of hospitalization and deaths. Identifying detrimental PCs would enable warnings and contraindications to be disseminated and assist the detection of unknown drug AEs. Nonetheless, the detection is difficult and has been limited to case reports or case assessment using administrative health claims data. Social media is a promising source for detecting signals of detrimental PCs due to the public availability of many discussions regarding treatments and drug AEs. OBJECTIVE: In this paper, we investigate the feasibility of detecting detrimental PCs from social media. METHODS: The detection, however, is challenging due to the data uncertainty and data rarity in social media. We propose a framework to mine sequences of drugs and AEs that signal detrimental PCs, taking into account the data uncertainty and data rarity. RESULTS: We conduct experiments on two real-world datasets collected from Twitter and Patient health forum. Our framework achieves encouraging results in the validation against known detrimental PCs (F1=78% for Twitter and 68% for Patient) and the detection of unknown potential detrimental PCs (Precision@50=72% and NDCG@50=95% for Twitter, Precision@50=86% and NDCG@50=98% for Patient). In addition, the framework is efficient and scalable to large datasets. CONCLUSION: Our study demonstrates the feasibility of generating hypotheses of detrimental PCs from social media to reduce pharmacists' guesswork.

Enhancing laboratory report contents to improve outpatient management of test results.

In today's environment, providers are extremely time-constrained. Assembling relevant contextual data to make decisions on laboratory results can take a significant amount of time from the day. The Regenstrief Institute has created a system which leverages data within Indiana Health Information Exchange's (IHIE's) repository, the Indiana Network for Patient Care (INPC), to provide well-organized and contextual information on returning laboratory results to outpatient providers. The system described here uses data extracted from INPC to add historical test results, medication-dispensing events, visit information, and clinical reminders to traditional laboratory result reports. These "Enhanced Laboratory Reports" (ELRs) are seamlessly delivered to outpatient practices connected through IHIE via the DOCS4DOCS clinical messaging service. All practices, including those without electronic medical record systems, can receive ELRs. In this paper, the design and implementation issues in creating this system are discussed, and generally favorable preliminary results of attitudes by providers towards ELRs are reported.