The Implicit Association Test in health professions education: A meta-narrative review

Introduction: Implicit bias is a growing area of interest among educators. Educational strategies used to elicit awareness of implicit biases commonly include the Implicit Association Test (IAT). Although the topic of implicit bias is gaining increased attention, emerging critique of the IAT suggests the need to subject its use to greater theoretical and empirical scrutiny. Methods: The authors employed a meta-narrative synthesis to review existing research on the use of the IAT in health professions education. Four databases were searched using key terms yielding 1151 titles. After title, abstract and full-text screening, 38 articles were chosen for inclusion. Coding and analysis of articles sought a meaningful synthesis of educational approaches relating to the IAT, and the assumptions and theoretical positions that informed these approaches. Results: Distinct, yet complementary, meta-narratives were found in the literature. The dominant perspective utilizes the IAT as a metric of implicit bias to evaluate the success of an educational activity. A contrasting narrative describes the IAT as a tool to promote awareness while triggering discussion and reflection. Discussion: Whether used as a tool to measure bias, raise awareness or trigger reflection, the use of the IAT provokes tension between distinct meta-narratives, posing a challenge to educators. Curriculum designers should consider the premise behind the IAT before using it, and be prepared to address potential reactions from learners such as defensiveness or criticism. Overall, findings suggest that educational approaches regarding implicit bias require critical reflexivity regarding assumptions, values and theoretical positioning related to the IAT

The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods

Why is the Winner the Best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work

MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbackComment: 16 page

Fludarabine, cytarabine, granulocyte colony-stimulating factor, and idarubicin with gemtuzumab ozogamicin improves event-free survival in younger patients with newly diagnosed aml and overall survival in patients with npm1 and flt3 mutations

Purpose To determine the optimal induction chemotherapy regimen for younger adults with newly diagnosed AML without known adverse risk cytogenetics. Patients and Methods One thousand thirty-three patients were randomly assigned to intensified (fludarabine, cytarabine, granulocyte colony-stimulating factor, and idarubicin [FLAG-Ida]) or standard (daunorubicin and Ara-C [DA]) induction chemotherapy, with one or two doses of gemtuzumab ozogamicin (GO). The primary end point was overall survival (OS). Results There was no difference in remission rate after two courses between FLAG-Ida + GO and DA + GO (complete remission [CR] + CR with incomplete hematologic recovery 93% v 91%) or in day 60 mortality (4.3% v 4.6%). There was no difference in OS (66% v 63%; P = .41); however, the risk of relapse was lower with FLAG-Ida + GO (24% v 41%; P < .001) and 3-year event-free survival was higher (57% v 45%; P < .001). In patients with an NPM1 mutation (30%), 3-year OS was significantly higher with FLAG-Ida + GO (82% v 64%; P = .005). NPM1 measurable residual disease (MRD) clearance was also greater, with 88% versus 77% becoming MRD-negative in peripheral blood after cycle 2 (P = .02). Three-year OS was also higher in patients with a FLT3 mutation (64% v 54%; P = .047). Fewer transplants were performed in patients receiving FLAG-Ida + GO (238 v 278; P = .02). There was no difference in outcome according to the number of GO doses, although NPM1 MRD clearance was higher with two doses in the DA arm. Patients with core binding factor AML treated with DA and one dose of GO had a 3-year OS of 96% with no survival benefit from FLAG-Ida + GO. Conclusion Overall, FLAG-Ida + GO significantly reduced relapse without improving OS. However, exploratory analyses show that patients with NPM1 and FLT3 mutations had substantial improvements in OS. By contrast, in patients with core binding factor AML, outcomes were excellent with DA + GO with no FLAG-Ida benefit

Velar–vowel Coarticulation in a Virtual Target Model of Stop Production

Velar–vowel coarticulation in English, resulting in so-called velar fronting in front vowel contexts, was studied using ultrasound imaging of the tongue during /k/ onsets of monosyllabic words with no coda or a labial coda. Ten native English speakers were recorded and analyzed. A variety of coarticulation patterns that often appear to contain small differences in typical closure location for similar vowels was found. An account of the coarticulation pattern is provided using a virtual target model of stop consonant production where there are two /k/ allophones in English, one for front vowels and one for non-front vowels. Small differences in closure location along the palate between productions within each context are the result of the trajectory of movement of the tongue from the vowel to vowel through the virtual target beyond the limit of the palate. The overall pattern is thus seen as a combination of a large planned allophonic difference between consonant closure targets and smaller phonetic differences for each particular vowel quality that are the result of coarticulation

Economics Of The Bioconversion Of Biomass To Methane And Other Vendable Products

This chapter highlights the sensitive cost factors (both technical and economic) which influence the profitability of anaerobic digestion yielding methane and other vendable products to the economics of energy production operative. A number of possibilities for vendable products and credits from anaerobic digestion have been suggested. Economic studies have been performed on the possibility of compressing the biogas and separating the carbon dioxide for sale, refeed of digester protein from dairy and steer fermentation, direct sale of digester solids as fertilizer, upgrading digester residues with supplemental nitrogen, phosphorus, and potassium to produce “organic fertilizer,” and on-site use in marketing of electricity from digester-produced methane. The data were derived from pilot-scale studies that had been verified as applicable to consistent performance of the process over relatively long periods of time. The results has been developed from over 1000 scenarios which have been run using computer outputs generated with the modified program