Preparing Humanities Students for Employment: Reimagining Career Exploration and Education through Ignatian Spirituality and Discernment

Graduate students in the humanities are hungry for career exploration as they face limited academic career options and feel called to work beyond the academy. Career preparation is typically left to graduate advisors, and then, the focus tends to be on academic career preparation. This article details how a required, introductory graduate class was reimagined to integrate career exploration using a framework at the heart of Ignatian spirituality and education: discernment. The authors outline the course and two assignments that can be adapted and applied to any graduate course. The authors share reflections on how the class has impacted their own professional and personal formation, and they end with heuristics for educators to use when preparing to teach similar graduate courses

Language Models for Hierarchical Classification of Radiology Reports with Attention Mechanisms, BERT and GPT-4

Radiology reports are a valuable source of textual information used to improve clinical care and support research. In recent years, deep learning techniques have been shown to be effective in classifying radiology reports. This article investigates the use of deep learning techniques with attention mechanisms to achieve better performance in the classification of radiology reports.We focus on various Natural Language Processing approaches, such as LSTM with Attention, BERT, and GPT-4, evaluated on a chest tomography report dataset regarding neoplastic diseases collected from an Italian hospital. In particular, we compare the results with a previous machine learning system, showing that models based on attention mechanisms can achieve higher performance. The Attention Mechanism allows us to identify the most relevant bits of text used by the model to make its predictions. We show that our model achieves state-of-the-art results on the hierarchical classification of radiology reports. Moreover, we evaluate the performance of GPT-4 on the classification of these reports in a zero-shot setup through prompt engineering, showing interesting results even with a small context and a non-English language. Our findings suggest that deep learning techniques with attention mechanisms may be successful in the classification of radiology reports even in non-English languages for which it is not possible to leverage on large text corpus

Recurrent Neural Networks for Daily Estimation of COVID-19 Prognosis with Uncertainty Handling

Most ML-based applications for COVID-19 assess the general conditions of a patient trained and tested on cohorts of patients collected over a short period of time and are capable of providing an alarm a few days in advance, helping clinicians in emergency situations, monitor hospitalised patients and identify potentially critical situations at an early stage. However, the pandemic continues to evolve due to new variants, treatments, and vaccines; considering datasets over short periods could not capture this aspect. In addition, these applications often avoid dealing with the uncertainty associated with the prediction provided by machine learning models, potentially causing costly mistakes. In this work, we present a system based on Recurrent Neural Networks (RNN) for the daily estimate of the prognosis of COVID-19 patients that is built and tested using data collected over a long period of time. Our system achieves high predictive performance and uses an algorithm to effectively determine and discard those patients for whom RNN cannot predict the prognosis with sufficient confidence

A Lab-scale Investigation of the Mars Kieffer Model

The Kieffer model is a widely accepted explanation for seasonal modification of the Martian surface by CO2 ice sublimation and the formation of a "zoo" of intriguing surface features. However, the lack of in situ observations and empirical laboratory measurements of Martian winter conditions hampers model validation and refinement. We present the first experiments to investigate all three main stages of the Kieffer model within a single experiment: (i) CO2 condensation on a thick layer of Mars regolith simulant; (ii) sublimation of CO2 ice and plume, spot, and halo formation; and (iii) the resultant formation of surface features. We find that the full Kieffer model is supported on the laboratory scale as (i) CO2 diffuses into the regolith pore spaces and forms a thin overlying conformal layer of translucent ice. When a buried heater is activated, (ii) a plume and dark spot develop as dust is ejected with pressurized gas, and the falling dust creates a bright halo. During plume activity, (iii) thermal stress cracks form in a network similar in morphology to certain types of spiders, dendritic troughs, furrows, and patterned ground in the Martian high south polar latitudes. These cracks appear to form owing to sublimation of CO2within the substrate, instead of surface scouring. We discuss the potential for this process to be an alternative formation mechanism for "cracked" spider-like morphologies on Mars. Leveraging our laboratory observations, we also provide guidance for future laboratory or in situ investigations of the three stages of the Kieffer model

Influence of physician empathy on the outcome of botulinum toxin treatment for upper limb spasticity in patients with chronic stroke: A cohort study.

To examine the relationship between patient-rated physician empathy and outcome of botulinum toxin treatment for post-stroke upper limb spasticity.Cohort study.Twenty chronic stroke patients with upper limb spasticity.All patients received incobotulinumtoxinA injection in at least one muscle for each of the following patterns: flexed elbow, flexed wrist and clenched fist. Each treatment was performed by 1 of 5 physiatrists with equivalent clinical experience. Patient-rated physician empathy was quantified with the Consultation and Relational Empathy Measure immediately after botulinum toxin treatment. Patients were evaluated before and at 4 weeks after botulinum toxin treatment by means of the following outcome measures: Modified Ashworth Scale; Wolf Motor Function Test; Disability Assessment Scale; Goal Attainment Scaling.Ordinal regression analysis showed a significant influence of patient-rated physician empathy (independent variable) on the outcome (dependent variables) of botulinum toxin treatment at 4 weeks after injection, as measured by Goal Attainment Scaling (p<0.001).These findings support the hypothesis that patient-rated physician empathy may influence the outcome of botulinum toxin treatment in chronic stroke patients with upper limb spasticity as measured by Goal Attainment Scaling

Patient-Specific Prosthetic Fingers by Remote Collaboration - A Case Study

The concealment of amputation through prosthesis usage can shield an amputee from social stigma and help improve the emotional healing process especially at the early stages of hand or finger loss. However, the traditional techniques in prosthesis fabrication defy this as the patients need numerous visits to the clinics for measurements, fitting and follow-ups. This paper presents a method for constructing a prosthetic finger through online collaboration with the designer. The main input from the amputee comes from the Computer Tomography (CT) data in the region of the affected and the non-affected fingers. These data are sent over the internet and the prosthesis is constructed using visualization, computer-aided design and manufacturing tools. The finished product is then shipped to the patient. A case study with a single patient having an amputated ring finger at the proximal interphalangeal joint shows that the proposed method has a potential to address the patient's psychosocial concerns and minimize the exposure of the finger loss to the public.Comment: Open Access articl