METHODS: The Z Printer 450 (3D Systems, Rock Hill, SC) reprinted 10 sets of models for each category of crowding (mild, moderate, and severe) scanned using a structured-light scanner (Maestro 3D, AGE Solutions, Pisa, Italy). Stone and RP models were measured using digital calipers for tooth sizes in the mesiodistal, buccolingual, and crown height planes and for arch dimension measurements. Bland-Altman and paired t test analyses were used to assess agreement and accuracy. Clinical significance was set at ±0.50 mm.
RESULTS: Bland-Altman analysis showed the mean bias of measurements between the models to be within ±0.15 mm (SD, ±0.40 mm), but the 95% limits of agreement exceeded the cutoff point of ±0.50 mm (lower range, -0.81 to -0.41 mm; upper range, 0.34 to 0.76 mm). Paired t tests showed statistically significant differences for all planes in all categories of crowding except for crown height in the moderate crowding group and arch dimensions in the mild and moderate crowding groups.
CONCLUSIONS: The rapid prototyping models were not clinically comparable with conventional stone models regardless of the degree of crowding.
METHODS: Electronic searches were conducted in the Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, MEDLINE (complete), PubMed and Scopus. Eligible studies to be included in this review were cohort studies with participants confirmed by laboratory test for dengue infection and comparison among the different severity of dengue infection by using statistical models. The methodological quality of the paper was assessed by independent reviewers using QUADAS-2.
RESULTS: Twenty-six studies published from 1994 to 2017 were included. Most diagnostic models produced an accuracy of 75% to 80% except one with 86%. Two models predicting severe dengue according to the WHO 2009 classification have 86% accuracy. Both of these logistic regression models were applied during the first three days of illness, and their sensitivity and specificity were 91-100% and 79.3-86%, respectively. Another model which evaluated the 30-day mortality of dengue infection had an accuracy of 98.5%.
CONCLUSION: Although there are several potential predictive or diagnostic models for dengue infection, their limitations could affect their validity. It is recommended that these models be revalidated in other clinical settings and their methods be improved and standardised in future.
MATERIALS AND METHODS: A prospective study was conducted at the single centre ICU in Hospital Sultanah Aminah (HSA) Malaysia. External validation of APACHE IV involved a cohort of 916 patients who were admitted in 2009. Model performance was assessed through its calibration and discrimination abilities. A first-level customisation using logistic regression approach was also applied to improve model calibration.
RESULTS: APACHE IV exhibited good discrimination, with an area under receiver operating characteristic (ROC) curve of 0.78. However, the model's overall fit was observed to be poor, as indicated by the Hosmer-Lemeshow goodness-of-fit test (Ĉ = 113, P <0.001). Predicted in-ICU mortality rate (28.1%) was significantly higher than the actual in-ICU mortality rate (18.8%). Model calibration was improved after applying first-level customisation (Ĉ = 6.39, P = 0.78) although discrimination was not affected.
CONCLUSION: APACHE IV is not suitable for application in HSA ICU, without further customisation. The model's lack of fit in the Malaysian study is attributed to differences in the baseline characteristics between HSA ICU and APACHE IV datasets. Other possible factors could be due to differences in clinical practice, quality and services of health care systems between Malaysia and the United States.