Evidence and risk indicators of non-random sampling in clinical trials in implant dentistry: A systematic appraisal.

AIM: Analysis of distribution of p-values of continuous differences between test and controls after randomization provides evidence of unintentional error, non-random sampling, or data fabrication in randomized controlled trials (RCTs). We assessed evidence of highly unusual distributions of baseline characteristics of subjects enrolled in clinical trials in implant dentistry. MATERIALS AND METHODS: RCTs published between 2005 and 2020 were systematically searched in Pubmed, Embase, and Cochrane databases. Baseline patient data were extracted from full text articles by two independent assessors. The hypothesis of non-random sampling was tested by comparing the expected and the observed distribution of the p-values of differences between test and controls after randomization. RESULTS: One-thousand five-hundred and thirty-eight unique RCTs were identified, of which 409 (26.6%) did not report baseline characteristics of the population, and 671 (43.6%) reported data in forms other than mean and standard deviation and could not be used to assess their random sampling. Four-hundred and fifty-eight trials with 1449 baseline variables in the form of mean and standard deviation were assessed. The study observed an over-representation of very small p-values [<.001, 1.38%, 95% confidence interval (CI) 0.85-2.12 compared to the expected 0.10%, 95% CI 0.00-0.26]. No evidence of over-representation of larger p-values was observed. Unusual distributions were present in 2.38% of RCTs and more frequent in non-registered trials, in studies supported by non-industry funding, and in multi-centre RCTs. CONCLUSIONS: The inability to assess random sampling due to insufficient reporting in 26.6% of trials requires attention. In trials reporting suitable baseline data, unusual distributions were uncommon, and no evidence of data fabrication was detected, but there was evidence of non-random sampling. Continued efforts are necessary to ensure high integrity and trust in the evidence base of the field.

Bovine-Derived Xenografts Immobilized With Cryopreserved Stem Cells From Human Adipose and Dental Pulp Tissues Promote Bone Regeneration: A Radiographic and Histological Study.

Adipose tissue-derived stem cells (ADSCs) and dental pulp stem cells (DPSCs) have become promising sources for bone tissue engineering. Our study aimed at evaluating bone regeneration potential of cryopreserved ADSCs and DPSCs combined with bovine-derived xenografts with 10% porcine collagen. In vitro studies revealed that although DPSCs had higher proliferative abilities, ADSCs exhibited greater mineral depositions and higher osteogenic-related gene expression, indicating better osteogenic differentiation potential of ADSCs. After applying cryopreserved ADSCs and DPSCs in a critical-sized calvarial defect model, both cryopreserved mesenchymal stem cells significantly improved bone volume density and new bone area at 2, 4, and 8 weeks. Furthermore, the combined treatment with ADSCs and xenografts was more efficient in enhancing bone repair processes compared to combined treatment with DPCSs at all-time points. We also evaluated the sequential early bone healing process both histologically and radiographically, confirming a high agreement between these two methods. Based on these results, we propose grafting of the tissue-engineered construct seeded with cryopreserved ADSCs as a useful strategy in accelerating bone healing processes.

[Contrast-enhanced CT for the diagnosis of extracapsular spread by head and neck cancers: a systematic review and meta analysis].

PURPOSE: This study aimed to assess the diagnostic efficacy of contrast-enhanced CT in the screening of extracapsular spread by head and neck cancers. METHODS: Electronic databases, including MEDLINE, EMBASE,CBM,CNKI and SciencePaper Online were searched electronically. Hand-searching was also performed. QUADAS-2 was used by two independent reviewers to assess the methodological quality, and data extraction of included studies was delivered. Meta analysis was conducted via MetaDisc1.4 and STATA 11.0. RESULTS: A total of 8 studies involving 639 participants were included. All studies were retrospective, 1 had high risk of bias, and the remaining had unclear risk of bias. Meta analysis showed that when screening extracapsular spread, contrast-enhanced CT had a pooled sensitivity of 0.67, pooled specificity of 0.84, area under curve of 0.83. CONCLUSIONS: Contrast-enhanced CT is a good tool for diagnosing extracapsular spread by head and neck cancers.

Evaluating oral epithelial dysplasia classification system by near-infrared Raman spectroscopy.

Until now, the classification system of oral epithelial dysplasia is still based on the architectural and cytological changes, which relies on the observation of pathologists and is relatively subjective. The purpose of present research was to discriminate the oral dysplasia by the near-infrared Raman spectroscope, in order to evaluate the classification system. We collected Raman spectra of normal mucosa, oral squamous cell carcinoma (OSCC) and dysplasia by near-infrared Raman spectroscope. The biochemical variations between different stages were analyzed by the characteristic peaks in the subtracted mean spectra. Gaussian radial basis function support vector machines (SVM) were used to establish the diagnostic models. At the same time, principal component analysis (PCA) and linear discriminant analysis (LDA) were used to verify the results of SVM. Raman spectral differences were observed in the range between 730~1913 cm-1. Compared with normal mucosa, high contents of protein and DNA in oral dysplasia and OSCC were observed. There were no significant or gradual variation of Raman peaks among different dysplastic grades. The accuracies of comparison between mild, moderate, severe dysplasia with OSCC were 100%, 44.44%, 71.15%, which elucidated the low modeling ability of support vector machines, especially for the moderate dysplasia. The analysis by PCA-LDA could not discriminate the stages, either. Combined with support vector machines, near-infrared Raman spectroscopy could detect the biochemical variations in oral normal, OSCC and dysplastic tissues, but could not establish diagnostic model accurately. The classification system needs further improvements.