Assessing physical fit examinations of stabbed and torn textiles through a large dataset of casework-like items and interlaboratory studies.

Several organizations have outlined the need for standardized methods for conducting physical fit comparisons. This study answers this call by developing and evaluating a systematic and transparent approach for examining, documenting, and interpreting textile physical fits, using qualitative feature descriptors and a quantitative metric (Edge Similarity Score, ESS) for the physical fit examination of textile materials. Here, the results from 1027 textile physical fit comparisons are reported. This includes the evaluation of inter and intraanalyst variation when using this method for hand-torn and stabbed fabrics. ESS higher than 80% and ESS lower than 20%, respectively, support fit and nonfit conclusions. The results show that analyst accuracy ranges from 88% to 100% when using this criterion. The estimated false-positive rate for this dataset (2% false positives, 10 of 477 true nonfit pairs) demonstrates the importance of assessing the quality of a physical fit during an examination and reveals that potential errors are low, but possible in textile physical fit examinations. The risk of error must be accounted for in the interpretation and verification processes. Further analysis shows that factors such as the separation method, construction, and design of the samples do not substantially influence the ESS values. Additionally, the proposed method is independently evaluated by 15 practitioners in an interlaboratory exercise that demonstrates satisfactory reproducibility between participants. The standardized terminology and documentation criteria are the first steps toward validating approaches to streamline the peer review process, minimize bias and subjectivity, and convey the probative value of the evidence.

Sharing Patient Praises With Radiology Staff: Workflow Automation and Impact on Staff.

OBJECTIVE: This study aims to develop and evaluate a semi-automated workflow using natural language processing (NLP) for sharing positive patient feedback with radiology staff, assessing its efficiency and impact on radiology staff morale. METHODS: The HIPAA-compliant, institutional review board-waived implementation study was conducted from April 2022 to June 2023 and introduced a Patient Praises program to distribute positive patient feedback to radiology staff collected from patient surveys. The study transitioned from an initial manual workflow to a hybrid process using an NLP model trained on 1,034 annotated comments and validated on 260 holdout reports. The times to generate Patient Praises e-mails were compared between manual and hybrid workflows. Impact of Patient Praises on radiology staff was measured using a four-question Likert scale survey and an open text feedback box. Kruskal-Wallis test and post hoc Dunn's test were performed to evaluate differences in time for different workflows. RESULTS: From April 2022 to June 2023, the radiology department received 10,643 patient surveys. Of those surveys, 95.6% contained positive comments, with 9.6% (n = 978) shared as Patient Praises to staff. After implementation of the hybrid workflow in March 2023, 45.8% of Patient Praises were sent through the hybrid workflow and 54.2% were sent manually. Time efficiency analysis on 30-case subsets revealed that the hybrid workflow without edits was the most efficient, taking a median of 0.7 min per case. A high proportion of staff found the praises made them feel appreciated (94%) and valued (90%) responding with a 5/5 agreement on 5-point Likert scale responses. CONCLUSION: A hybrid workflow incorporating NLP significantly improves time efficiency for the Patient Praises program while increasing feelings of acknowledgment and value among staff.

Complete genome sequence of the Streptomyces bacteriophage Amabiko.

Amabiko is a lytic subcluster BE2 bacteriophage that infects Streptomyces scabiei-a bacterium causing common scab in potatoes. Its 131,414 bp genome has a GC content of 49.5% and contains 245 putative protein-coding genes, 45 tRNAs, and one tmRNA. Amabiko is closely related to Streptomyces bacteriophage MindFlayer (gene content similarity: 86.5%).