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Objective: To evaluate the repeatability, interexaminer, and interdevice reliability of two clinically applicable spectrophotometers under laboratory and clinical conditions.Material and Methods: For the in vitro part of the study, measurements were performed by the use of Vita Easyshade Advance 4.0 (ES-A) and the Easyshade V (ES-V) at identical positions on different shade tabs (3D-Master; Vita Zahnfabrik, Bad Säckingen, Germany). To test repeatability, one shade tab was measured 50 times by one operator. To determine interrater and interdevice agreement, two operators used both devices to perform 10 measurements on five different shade tabs. Clinical interdevice and interexaminer reliability was checked with a positioning jig used (15 participants). Measurement accuracy of both devices was evaluated for the recommended color of shade tabs.Results: Repeatability of results from both Easyshades was excellent for all color components (maximum deviation between measurements was ≤0.1 units). Interrater agreement was also perfect (intraclass correlation, ICC = 1.000). Interdevice agreement was lower, but still good (ICC ≥ 0.834). In the clinical environment, interrater and interdevice agreements were similar (ICC > 0.964 and ICC > 0.873). Accuracy was satisfactory for both devices, with both observers in full agreement for nearly 80% of ratings.Conclusions: Both Easyshades produced reliable and accurate measurements and can therefore be recommended for clinical determination of tooth color.

Objective: To compare the 3-year survival and success rates of monolithic (M) and partially veneered (PV) zirconia-fixed partial dentures (FPDs). Materials and Methods:Sixty-seven FPDs (n = 33 M-FPDs; n = 34 PV-FPDs) were placed in 51 patients (n = 23 males; mean age 61.5 years) and clinically followed up 1 week, 6 months, and then yearly after placement. One hundred per cent (100%) of M-FPDs and 70% of PV-FPDs were located in the posterior region. Ninety-two per cent (92%) of M-FPDs had three units, whereas 50% of PV-FPDs had more than three units.A facial veneer was present in 73% of the PV-FPDs units. Survival and success were calculated using the Kaplan-Meier method and compared using the log-rank test (α = .05). Results: The mean observation period was 3.5 years for M-FPDs and 3.1 years for PV-FPDs. Most complications associated with FPDs were biological in nature. Ceramic defects occurred exclusively among PV-FPDs. Three-year survival was 96.7% for M-FPDs and 93.8% for PV-FPDs (P = .064). Three-year success was 93.8% for M-FPDs and 81.7% for PV-FPDs (P = .039). Conclusions: The use of both M-FPDs and PV-FPDs yielded clinically successful results over a mean period of 3 years.Clinical Significance: By using monolithic or facially veneered zirconia, ceramic FPDs could be fabricated which showed only a minimum of technical complications over the period of investigation without sacrificing adequate esthetics.

Purpose This study aimed to develop and validate machine-learning models for the prediction of recurrent infection in patients following revision total knee arthroplasty for periprosthetic joint infection. Methods A total of 618 consecutive patients underwent revision total knee arthroplasty for periprosthetic joint infection. The patient cohort included 165 patients with confirmed recurrent periprosthetic joint infection (PJI). Potential risk factors including patient demographics and surgical characteristics served as input to three machine-learning models which were developed to predict recurrent periprosthetic joint. The machine-learning models were assessed by discrimination, calibration and decision curve analysis. ResultsThe factors most significantly associated with recurrent PJI in patients following revision total knee arthroplasty for PJI included irrigation and debridement with/without modular component exchange (p < 0.001), > 4 prior open surgeries (p < 0.001), metastatic disease (p < 0.001), drug abuse (p < 0.001), HIV/AIDS (p < 0.01), presence of Enterococcus species (p < 0.01) and obesity (p < 0.01). The machine-learning models all achieved excellent performance across discrimination (AUC range 0.81-0.84). Conclusion This study developed three machine-learning models for the prediction of recurrent infections in patients following revision total knee arthroplasty for periprosthetic joint infection. The strongest predictors were previous irrigation and debridement with or without modular component exchange and prior open surgeries. The study findings show excellent model performance, highlighting the potential of these computational tools in quantifying increased risks of recurrent PJI to optimize patient outcomes. Level of evidence IV.