An experience of multidisciplinary tutorials sessions about antibiotics in the third year of pharmacy studies in Angers, France: learning assessment and evaluation of students' feelings by a mixed approach.

For undergraduate pharmacy students, the first step of antimicrobial stewardship learning objectives is to integrate antimicrobial knowledge from the foundational sciences. We hypothesised that using a multidisciplinary approach including two sessions of tutorials could be relevant in term of students' interest, satisfaction and learning retention time. The evaluation of students' feelings was based on a questionnaire including different dimensions and three focus groups with four students. Quantitative data were analysed with the EPI-INFO 7.2 software and a thematic analysis was implemented for qualitative data by using NVivo 12 software. The evaluation of students' learning concerned both short-time learning retention (STLR) and medium-time learning retention (MTLR), six months after the last session. Overall, 63 students responded to the questionnaire. Most of them appreciated the tutorials according to the different dimensions envisaged. Focus groups confirmed the interest of students for the multidisciplinary approach, interactions with teachers and opportunities of learning transfers. Concurrently, a lack of self-efficacy, low confidence towards the other students, external regulation of motivation and poor autonomy were recorded for some participants. Finally, there was no significant decrease between the scores of the STLR assessment and those of the MTLR assessment (58.5 ± 12.1/100 and 54.4 ± 8.9/100, respectively).

Achieving Expert-Level Interpretation of Serum Protein Electrophoresis through Deep Learning Driven by Human Reasoning.

BACKGROUND: Serum protein electrophoresis (SPE) is a common clinical laboratory test, mainly indicated for the diagnosis and follow-up of monoclonal gammopathies. A time-consuming and potentially subjective human expertise is required for SPE analysis to detect possible pitfalls and to provide a clinically relevant interpretation. METHODS: An expert-annotated SPE dataset of 159 969 entries was used to develop SPECTR (serum protein electrophoresis computer-assisted recognition), a deep learning-based artificial intelligence, which analyzes and interprets raw SPE curves produced by an analytical system into text comments that can be used by practitioners. It was designed following academic recommendations for SPE interpretation, using a transparent architecture avoiding the "black box" effect. SPECTR was validated on an external, independent cohort of 70 362 SPEs and challenged by a panel of 9 independent experts from other hospital centers. RESULTS: SPECTR was able to identify accurately both quantitative abnormalities (r ≥ 0.98 for fractions quantification) and qualitative abnormalities [receiver operating characteristic-area under curve (ROC-AUC) ≥ 0.90 for M-spikes, restricted heterogeneity of immunoglobulins, and beta-gamma bridging]. Furthermore, it showed highly accurate at both detecting (ROC-AUC ≥ 0.99) and quantifying (r = 0.99) M-spikes. It proved highly reproducible and resilient to minor variations and its agreement with human experts was higher (κ = 0.632) than experts between each other (κ = 0.624). CONCLUSIONS: SPECTR is an algorithm based on artificial intelligence suitable to high-throughput SPEs analyses and interpretation. It aims at improving SPE reproducibility and reliability. It is freely available in open access through an online tool providing fully editable validation assistance for SPE.