Clinical effect of the high insertion torque on dental implants: A systematic review and meta-analysis. Lemos CA, Verri FR, de Oliveira Neto OB, Cruz RS, Gomes JML, da Silva Casado BG, Pellizzer EP. J Prosthet Dent 2021; 126(4): 490-496.
Not reported.
Systematic review with meta-analysis (SR).

LIMITED EVIDENCE SUGGESTS NO CORRELATION BETWEEN IMPLANT INSERTION TORQUE AND IMPLANT SURVIVAL AND MARGINAL BONE LOSS.

Insulin resistance (IR) is considered the precursor and the key pathophysiological mechanism of type 2 diabetes (T2D) and metabolic syndrome (MetS). However, the pathways that IR shares with T2D are not clearly understood. Meta-analysis of multiple DNA microarray datasets could provide a robust set of metagenes identified across multiple studies. These metagenes would likely include a subset of genes (key metagenes) shared by both IR and T2D, and possibly responsible for the transition between them. In this study, we attempted to find these key metagenes using a feature selection method, LASSO, and then used the expression profiles of these genes to train five machine learning models: LASSO, SVM, XGBoost, Random Forest, and ANN. Among them, ANN performed well, with an area under the curve (AUC) > 95%. It also demonstrated fairly good performance in differentiating diabetics from normal glucose tolerant (NGT) persons in the test dataset, with 73% accuracy across 64 human adipose tissue samples. Furthermore, these core metagenes were also enriched in diabetes-associated terms and were found in previous genome-wide association studies of T2D and its associated glycemic traits HOMA-IR and HOMA-B. Therefore, this metagenome deserves further investigation with regard to the cardinal molecular pathological defects/pathways underlying both IR and T2D.

Machine Learning Model Based on Insulin Resistance Metagenes Underpins Genetic Basis of Type 2 Diabetes.

Artificial intelligence (AI) has potential uses in healthcare including the detection of health conditions and prediction of health outcomes. Past systematic reviews had reviewed the accuracy of artificial neural networks (ANN) on Electrocardiogram (ECG) readings but that of other AI models on other Acute Coronary Syndrome (ACS) detection tools remains unclear.
Nine electronic databases were searched from 2012 to 31 August 2022 including grey literature search and hand searching of references of included articles. Risk of bias was assessed by two independent reviewers using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Test characteristics namely true positives, false positives, true negatives, and false negatives were extracted from all included articles into a 2x2 table. Study-specific estimates of sensitivity and specificity were pooled using hierarchical summary receiver operating characteristic (HSROC) model and displayed using a forest plot and HSROC curve.
66 studies were included in the review. A total of 518,931 patients were included whose mean ages varied from 32.62 to 70 years old. In 66 studies, the sensitivity and specificity of AI-based detection for ACS screening ranged from 64 % to 100 % and 65 %-100 %, respectively. The overall quality of evidence was low due to the inclusion of case-control studies.
Results of the study inform the potential of using AI-assisted ACS detection for accurate diagnosis and prompt treatment for ACS. Adherence to the Standards for Reporting of Diagnostic Accuracy (STARD) guideline and having more cohort studies for future Diagnostic Test Accuracy (DTA) studies are necessary to improve the quality of evidence of AI-based detection of ACS.

Diagnostic Test Accuracy of artificial intelligence-assisted detection of acute coronary syndrome: A systematic review and meta-analysis.

Some conceptualizations of hypersexuality posit boredom as a possible trigger of hypersexual behavior.
This work aims to review published articles addressing the link between boredom and hypersexuality to investigate if this relationship can yet be established based on current empirical data.
This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies, published until September 2019, were retrieved from EBSCO, Scopus, Web of Science, and PubMed. A systematic search was conducted using an exhaustive list of key terms combining "boredom" with "hypersexuality," "sexual impulsivity," "sexual compulsivity," and "sexual addiction." Only articles presenting empirical results concerning the relationship between boredom and hypersexuality were considered.
From the initial poll of 76 articles, only 19 articles were included in our final selection. Of the total studies, 16 were quantitative studies and 3 were qualitative studies. 4 studies were validation studies of hypersexuality-related measures, 11 studies concerned online sexual activity, and 3 reported sexual boredom. 7 studies with men used non-heterosexual samples. 5 studies used samples with both women and men, and 1 study used a sample of women only. Most studies indicate a positive association between boredom and hypersexuality, although 4 did not.
Further studies with diversified samples are still indispensable, as female samples are not portrayed and research is much focused on online sexual activity. Future research should explore the relationship between boredom and hypersexuality within specific behavioral specifiers including masturbation, pornography use, sexual behavior with consenting adults, cybersex, telephone sex, and strip clubs.
To the authors' knowledge, this is the first review looking at the possible link between boredom and hypersexuality. Investigation on the topic is scarce, and several of the studies included in this review correspond to anecdotal evidences of the phenomenon, as only few studies used appropriate measures of boredom.
Although current literature identifies a link between boredom and hypersexuality, further substantive research is still much needed to clarify the associations between the 2 constructs. de Oliveira L, Carvalho J. The Link Between Boredom and Hypersexuality: A Systematic Review. J Sex Med 2020;17:994-1004.

The Link Between Boredom and Hypersexuality: A Systematic Review.

Oral cancer can show heterogenous patterns of behavior. For proper and effective management of oral cancer, early diagnosis and accurate prediction of prognosis are important. To achieve this, artificial intelligence (AI) or its subfield, machine learning, has been touted for its potential to revolutionize cancer management through improved diagnostic precision and prediction of outcomes. Yet, to date, it has made only few contributions to actual medical practice or patient care.
This study provides a systematic review of diagnostic and prognostic application of machine learning in oral squamous cell carcinoma (OSCC) and also highlights some of the limitations and concerns of clinicians towards the implementation of machine learning-based models for daily clinical practice.
We searched OvidMedline, PubMed, Scopus, Web of Science, and Institute of Electrical and Electronics Engineers (IEEE) databases from inception until February 2020 for articles that used machine learning for diagnostic or prognostic purposes of OSCC.
Only original studies that examined the application of machine learning models for prognostic and/or diagnostic purposes were considered.
Independent extraction of articles was done by two researchers (A.R. & O.Y) using predefine study selection criteria. We used the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) in the searching and screening processes. We also used Prediction model Risk of Bias Assessment Tool (PROBAST) for assessing the risk of bias (ROB) and quality of included studies.
A total of 41 studies were published to have used machine learning to aid in the diagnosis/or prognosis of OSCC. The majority of these studies used the support vector machine (SVM) and artificial neural network (ANN) algorithms as machine learning techniques. Their specificity ranged from 0.57 to 1.00, sensitivity from 0.70 to 1.00, and accuracy from 63.4 % to 100.0 % in these studies. The main limitations and concerns can be grouped as either the challenges inherent to the science of machine learning or relating to the clinical implementations.
Machine learning models have been reported to show promising performances for diagnostic and prognostic analyses in studies of oral cancer. These models should be developed to further enhance explainability, interpretability, and externally validated for generalizability in order to be safely integrated into daily clinical practices. Also, regulatory frameworks for the adoption of these models in clinical practices are necessary.

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