SOCIAL WORKER PERCEPTIONS OF EQUINE ASSISTED PSYCHOTHERAPY

This research study examines factors that influence a social worker’s willingness to utilize animals in therapy, specifically equine assisted psychotherapy. Equine assisted psychotherapy is an experiential therapy involving horses for the treatment of mental and behavioral health issues. The study uses quantitative data. The sample population is Bachelors of Social Work (BSW) and Masters of Social Work (MSW) students attending a University in Southern California. The participants were provided an electronic self-administered survey through their University email account. The data collected was analyzed and the results were provided to the University. The results indicate a relationship between several variables, such as previously owning and/or caring for a pet and fondness of animals, however, there are likely other factors that predict the use of equine assisted therapy that were not explored in this study. The results of this study will help raise awareness about equine assisted psychotherapy and the benefits of utilizing this non-traditional treatment

Racine Woolen Manufacturing Company Bead Sample Card

This undated card from Racine Woolen Manufacturing Company includes samples of different colors and sizes of small glass beads available for purchase.https://commons.und.edu/burdick-papers/1057/thumbnail.jp

Construction of a unit of programmed instruction in health education

The purpose of this study was to construct and validate a programmed unit of instruction on the human circulatory system. This program was designed for use in basic health education courses on the college level. A secondary purpose of the study was to obtain an observation of the application of programmed instruction among college students engaged in the study of basic health education. Sixty-seven freshmen and sophomore women from The University of North Carolina at Greensboro were used as subjects. The material for the program content was classified into five parts and program objectives were formulated from the predetermined material. The program frames were constructed in strict accordance with the program objectives

Long-Term Efficacy and Tolerability of Abdominal Once-Yearly Histrelin Acetate Subcutaneous Implants in Patients with Advanced Prostate Cancer

Objectives. Long-term assessment of the efficacy and tolerability of subcutaneous abdominal histrelin acetate implants that have been inserted for more than two years. Materials and Methods. Retrospective data collected over a six-year period at a single center from charts of 113 patients who received the subcutaneous abdominal histrelin acetate implant. Results. Following insertion of the first implant, 92.1% and 91.8% of patients had a serum testosterone level of ≤30 ng/dL at 24 and 48 weeks, respectively. Serum testosterone levels remained at <30 ng/dL for 96% of patients at two years and for 100% of patients at 3, 4, and 5 years. The testosterone levels remained significantly less than baseline (P<0.05). Six patients (5.3%) had androgen-independent progression when followed up on the long term, increasing the mean serum PSA at 3, 4, and 5 years to 35.0 µg/L (n=22), 30.7 µg/L (n=13), and 132.9 µg/L (n=8), respectively. The mean serum PSA was significantly greater than baseline during these years (P<0.05). Eight patients (7.1%) experienced minor, but not serious, adverse events from the histrelin acetate. Conclusion. Subcutaneous abdominal histrelin acetate implants are an effective long-term and well-tolerated administration method for treating patients with advanced prostate cancer

Smallholder Agriculture Monitoring and Baseline Assessment (SHAMBA) tool, Version 1.0

The SHAMBA (Small-Holder Agriculture Mitigation Benefit Assessment) model estimates greenhouse gas (GHG) emissions or removals resulting from a change in land management practices. SHAMBA is designed to model a baseline scenario (where land management activities continue as business as usual) and an intervention scenario consisting of activities that can be described as Climate Smart Agricultural practices (CSA) including, conservation agriculture, agroforestry and other tree planting. SHAMBA models the changes in carbon stocks in soils and woody biomass, and the GHG emissions from biomass burning, plant nitrogen inputs to soils, and fertiliser use over the accounting period for baseline and intervention activities. Net emissions and removals are calculated on a yearly basis for the length of the accounting period, in units of tonnes (t) of carbon dioxide equivalent (CO2e) per hectare (ha). Version one of the SHAMBA model is designed to work with smallholder systems and is available at https://shambatool.wordpress.com/outputs/

Precise prostate contours: setting the bar and meticulously evaluating AI performance

Abstract: Introduction: Evaluation of artificial intelligence (AI) algorithms for prostate segmentation is challenging because ground truth is lacking. We aimed to (1) create a reference standard dataset with precise prostate contours by expert consensus and (2) evaluate various AI tools against this standard. Materials and methods: We obtained prostate MRI cases from six institutions from the Quantitative Prostate Imaging Consortium. A panel of four experts (two genitourinary radiologists, two prostate radiation oncologists) meticulously developed consensus prostate segmentations on axialT2-weighted series. We evaluated the performance of six AI tools (three commercially available, three academic) using Dice scores, distance from reference contour, and volume error. Results: The panel achieved consensus prostate segmentation on each slice of all 68 patient cases included in the reference dataset. We present two patient examples to serve as contouring guides. Depending on the AI tool, median Dice scores (across patients) ranged from 0.80 to 0.94 for whole prostate segmentation. For a typical (median) patient, AI tools had a mean error over the prostate surface ranging from 1.3 to 2.4 mm. They maximally deviated 3.0 to 9.4 mm outside the prostate and 3.0 to 8.5 mm inside the prostate for a typical patient. Error in prostate volume measurement for a typical patient ranged from 4.3% to 31.4%. Discussion: We established an expert consensus benchmark for prostate segmentation. The best-performing AI tools have typical accuracy greater than that reported for radiation oncologists using CT scans (most common clinical approach for radiotherapy planning). Physician review remains essential to detect occasional major errors

Advanced Restriction imaging and reconstruction Technology for Prostate MRI (ART-Pro): Study protocol for a multicenter, multinational trial evaluating biparametric MRI and advanced, quantitative diffusion MRI for detection of prostate cancer

Abstract: Background: Multiparametric MRI (mpMRI) is strongly recommended by current clinical guidelines for improved detection of clinically significant prostate cancer (csPCa). However, major limitations of mpMRI are the need for intravenous (IV) contrast and dependence on reader expertise. Efforts to address these issues include use of biparametric MRI (bpMRI) and advanced, quantitative MRI techniques. One such advanced technique is the Restriction Spectrum Imaging restriction score (RSIrs), an imaging biomarker that has been shown to improve quantitative accuracy of patient-level csPCa detection. Purpose: To evaluate whether IV contrast can be avoided in the setting of standardized, state-of-the-art image acquisition, with or without addition of RSIrs, and to evaluate characteristics of RSIrs as a stand-alone, quantitative biomarker. Design, setting, and participants: ART-Pro is a multisite, multinational trial that will be conducted in two stages, evaluating bpMRI, mpMRI, and RSIrs on accuracy of expert (ART-Pro-1) and non-expert (ART-Pro-2) radiologists’ detection of csPCa. Additionally, RSIrs will be evaluated as a stand-alone, quantitative, objective biomarker (ART-Pro-1). This study will include a total of 500 patients referred for a multiparametric prostate MRI with a clinical suspicion of prostate cancer at any of the five participating sites (100 patients per site). Intervention: In ART-Pro-1, patients receive standard of care mpMRI, with addition of the RSI sequence, and subsets of the patients’ images are read separately by two expert radiologists, one of whom is the standard of care radiologist (Reader 1). Three research reports are generated using: bpMRI only (Reader 1), mpMRI (Reader 1), and bpMRI + RSIrs (Reader 2). The clinical report is submitted by Reader 1. Patients’ future prostate cancer management will be recorded and used to evaluate the performance of the MRI techniques being tested. In ART-Pro-2, the dataset created in ART-Pro-1 will be retrospectively reviewed by radiologists of varying experience level (novice, basic, and expert). Radiologists will be assigned to read cases and record research reports while viewing subsets of either mpMRI only or RSIrs + mpMRI. Patient cases will be read by two readers from each experience level (6 reads total), and findings will be evaluated against the expertly created dataset from ART-Pro-1. Outcome measurements and statistical analysis: The primary endpoint is to evaluate if bpMRI is non-inferior to mpMRI among expert radiologists (ART-Pro-1) and non-expert radiologists (ART-Pro-2) for detection of grade group (GG) ≥2 csPCa. We will conduct one-sided non-inferiority tests of correlated proportions (ART-Pro-1) and use McNemar’s test and AUC to test the null hypothesis of non-inferiority (ART-Pro-1 and ART-Pro-2). Conclusions: This trial is registered in the US National Library of Medicine Trial Registry (NCT number:NCT06579417) atClinicalTrials.gov. Patient accrual at the first site (UC San Diego) began in December 2023. The expected trial timeline is three years to complete accrual with a six-month endpoint

Deep learning AI and Restriction Spectrum Imaging for patient-level detection of clinically significant prostate cancer on MRI

Abstract: Background: The Prostate Imaging Reporting &amp; Data System (PI-RADS), based on multiparametric MRI (mpMRI), is widely used for the detection of clinically significant prostate cancer (csPCa, Gleason Grade Group (GG≥2)). However, its diagnostic accuracy can be impacted by variability in interpretation. Restriction Spectrum Imaging (RSI), an advanced diffusion-weighted technique, offers a standardized, quantitative approach for detecting csPCa, potentially enhancing diagnostic consistency and performing comparably to expert-level assessments. Purpose: To evaluate whether combining maximum RSI-derived restriction scores (RSIrs-max) with deep learning (DL) models can enhance patient-level detection of csPCa compared to using PI-RADS or RSIrs-max alone. Materials and Methods: Data from 1,892 patients across seven institutions were analyzed, selected based on MRI results and biopsy-confirmed diagnoses. Two deep learning architectures, 3D-DenseNet and 3D-DenseNet+RSI (incorporating RSIrs-max), were developed and trained using biparametric MRI (bpMRI) and RSI data across two data splits. Model performance was compared using the area under the receiver operating characteristic curve (AUC) for patient-level csPCa detection, using PI-RADS performance for clinical reference. Results: Neither RSIrs-max nor the best DL model combined with RSIrs-max significantly outperformed PI-RADS interpretation by expert radiologists. However, when combined with PI-RADS, both approaches significantly improved patient-level csPCa detection, with AUCs of 0.79 (95% CI: 0.74-0.83;P=.005) for combination of RSIrs-max with PI-RADS and 0.81 (95% CI: 0.76-0.85;P&lt;.001) for combination of best DL model with PI-RADS, compared to 0.73 (95% CI: 0.68-0.78) for PI-RADS alone. Conclusion: Both RSIrs-max and DL models demonstrate comparable performance to PI-RADS alone. Integrating either model with PI-RADS significantly enhances patient-level detection of csPCa compared to using PI-RADS alone. Summary Statement: RSIrs-max and deep learning models match the performance of expert PI-RADS in patient-level csPCa detection and combining either with PI-RADS yields a significant improvement over PI-RADS alone. Key Points: In a study of 1,892 patients from seven institutions undergoing MRI and biopsy for prostate cancer, RSIrs-max and the DL model (AUC, 0.75 (P=.59) and 0.78 (P=.09)) performed comparably to expert-level PI-RADS scores (AUC, 0.73). Including prostate auto-segmentation improved the DL model (AUC, 0.68 (P=.01) vs 0.72 (P=.60)). Combining RSIrs-max or the DL model (AUC, 0.79 (P=.005) and 0.81 (P&lt;.001)) with PI-RADS statistically significantly outperformed PI-RADS alone (AUC, 0.73)

Restriction Spectrum Imaging as a quantitative biomarker for prostate cancer with reliable positive predictive value

Abstract: Background and Objective: Positive predictive value of PI-RADS for clinically significant prostate cancer (csPCa, grade group [GG]≥2) varies widely between institutions and radiologists. The Restriction Spectrum Imaging restriction score (RSIrs) is a metric derived from diffusion MRI that could be an objectively interpretable biomarker for csPCa. Methods: In patients scanned for suspected or known csPCa at 7 centers, we calculated patient-level csPCa probability based on maximum RSIrs in the prostate, without relying on subjectively defined lesions. We used area under the ROC curve (AUC) to compare patient-level csPCa detection for RSIrs, ADC, and PI-RADS. Finally, we combined RSIrs with clinical risk factors via multivariable regression, training in a single-center cohort and testing in an independent, multi-center dataset. Key Findings and Limitations: Among all patients (n=1892), probability of csPCa increased with higher RSIrs . GG≥4 csPCa was most common in patients with very high RSIrs. Among biopsy-naïve patients (n=877), AUCs for GG≥2 vs. non-csPCa were 0.73 (0.69-0.76), 0.54 (0.50-0.57), and 0.75 (0.71-0.78) for RSIrs, ADC, and PI-RADS, respectively. RSIrs significantly outperformed ADC (p&lt;0.01) and was comparable to PI-RADS (p=0.31). The combination of RSIrs and PI-RADS outperformed either alone. Combining RSIrs with PI-RADS, age, and PSA density in a multivariable model achieved the best discrimination of csPCa. Conclusions and Clinical Implications: RSIrs is an accurate and reliable quantitative biomarker that performs better than conventional ADC and comparably to expert-defined PI-RADS for patient-level detection of csPCa. RSIrs provides objective estimates of probability of csPCa that do not require radiology expertise