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High-translucency restorative materials are commonly used in the restoration of anterior teeth where aesthetics is a critical factor. In this in vitro study, the impact of mouthwash on the colour stability and surface characteristics of high-translucency computer-aided design and computer-aided manufacturing (CAD-CAM) dental restorative materials was evaluated. Two-hundred specimens were fabricated from five high-translucency CAD-CAM materials: a resin nano ceramic; a polymer-infiltrated ceramic network; a feldspathic ceramic; a lithium disilicate glass ceramic; and high-translucency zirconia. Each group of ceramic specimens was then divided into four subgroups: conventional mouthwash (LISTERINE); whitening mouthwash (LISTERINE Healthy White); chlorhexidine gluconate; and distilled water. Oral rinsing was simulated at 100 rpm for 180 h, representing 15 yr of clinical simulation. The specimens were then evaluated for colour, translucency, gloss, roughness, and surface morphology. Two-way ANOVA and linear mixed models were used for intergroup comparisons (α = 0.05). The polymer-infiltrated ceramic network and feldspathic ceramic became brighter, more opaque, less glossy, and rougher after rinsing with the whitening mouthwash. The long-term use of specific mouthwashes can cause deterioration of the optical and surface properties of high-translucency CAD-CAM dental restorations.

ImportanceTemporal dataset shift associated with changes in healthcare over time is a barrier to deploying machine learning-based clinical decision support systems. Algorithms that learn robust models by estimating invariant properties across time periods for domain generalization (DG) and unsupervised domain adaptation (UDA) might be suitable to proactively mitigate dataset shift.ObjectiveTo characterize the impact of temporal dataset shift on clinical prediction models and benchmark DG and UDA algorithms on improving model robustness.Design, Setting, and ParticipantsIn this cohort study, intensive care unit patients from the MIMIC-IV database were categorized by year groups (2008–2010, 2011–2013, 2014–2016 and 2017–2019). Tasks were predicting mortality, long length of stay, sepsis and invasive ventilation. Feedforward neural networks were used as prediction models. The baseline experiment trained models using empirical risk minimization (ERM) on 2008–2010 (ERM[08-10]) and evaluated them on subsequent year groups. DG experiment trained models using algorithms that estimated invariant properties using 2008–2016 and evaluated them on 2017– 2019. UDA experiment leveraged unlabelled samples from 2017–2019 for unsupervised distribution matching. DG and UDA models were compared to ERM[08-16] models trained using 2008-2016.Main Outcome(s) and Measure(s)Main performance measures were area-under-the-receiver-operating-characteristic curve (AUROC), area-under-the-precision-recall curve and absolute calibration error. Threshold-based metrics including false-positives and false-negatives were used to assess the clinical impact of temporal dataset shift and its mitigation strategies.ResultsIn the baseline experiments, dataset shift was most evident for sepsis prediction (maximum AUROC drop, 0.090; 95% confidence interval (CI), 0.080-0.101). Considering a scenario of 100 consecutively admitted patients showed that ERM[08-10] applied to 2017-2019 was associated with one additional false-negative among 11 patients with sepsis, when compared to the model applied to 2008-2010. When compared with ERM[08-16], DG and UDA experiments failed to produce more robust models (range of AUROC difference, −0.003-0.050).Conclusions and RelevanceDG and UDA failed to produce more robust models compared to ERM in the setting of temporal dataset shift. Alternate approaches are required to preserve model performance over time in clinical medicine.KEY POINTSQuestionCan algorithms that estimate invariant properties across environments for domain generalization and unsupervised domain adaptation improve the robustness of machine learning-derived clinical prediction models to temporal dataset shift?FindingsIn this cohort study using 4 clinical outcomes, domain generalization and unsupervised domain adaptation algorithms did not meaningfully outperform the standard model training algorithm – empirical risk minimization – in learning robust models that generalize over time in the presence of temporal dataset shift.MeaningThese findings highlight the difficulty of improving robustness to dataset shift with purely data-driven techniques that do not leverage prior knowledge of the nature of the shift and the requirement of alternate approaches to preserve model performance over time in clinical medicine.

38th European Photovoltaic Solar Energy Conference and Exhibition; 640-645, Bifacial PV modules are promising to achieve a lower levelized cost of electricity (LCOE) compared to monofacial modules because light absorption occurs from both sides of the devices. Compared to the glass/glass bifacial modules, glass/backsheet modules show many advantages including lighter weight and high corrosion resistance. However, research on the weatherability and long-term reliability of transparent backsheet materials and bifacial modules under their service environments is lacking. In this study, accelerated weather testing using the NIST SPHERE (Simulated Photodegradation via High Energy Radiant Exposure) was conducted to investigate the durability of three fluoropolymer-based transparent backsheets and their laminated coupon counterparts. The transparent backsheets were exposed at UV/75 C/50 % relative humidity (RH), while the coupons were exposed at UV/65 C/50 % RH, both subjected to UV irradiance of approximately 140 W/m2 for up to 2000 h. Optical, chemical properties and mechanical properties of specimens were characterized. Results indicate that different transparent backsheets perform differently after aging, and potential cracking could occur once films become brittle. The cracking of polyethylene terephthalate (PET) core layer was observed, as well as the cracking of the ethylene vinyl-acetate (EVA) inner layer for a fluoroethylene vinyl ether (FEVE)/ PET/ EVA transparent backsheet. The other two backsheets, polyvinyl fluoride (PVF)/PET/FEVE and polyvinylidene fluoride (PVDF)/PET/FEVE, showed no obvious signs of cracking up to 2000 h of UV exposure. This study suggests that UV stability of PET core layer is critical to the application of transparent backsheets for bifacial PV modules.