Immediately provisionalized tapered conical connection implants for single-tooth restorations in the maxillary esthetic zone: a 5-year prospective single-cohort multicenter analysis

Objectives This open, single-cohort, multicenter, prospective study investigated the efficacy of immediately provisionalized tapered conical connection implant for single-tooth restorations in the anterior and premolar regions of the maxilla after 5 years of function. Materials and methods All implants were placed in healed sites and immediately provisionalized. MBLs, soft-tissue parameters, and oral-health impact profile (OHIP) were evaluated at implant insertion, 6, 12, 24, 36, and 60 months. Paired Wilcoxon signed-rank tests and Kaplan–Meier survival analysis was used for statistical and implant survival/success analyses, respectively. Results Seventy-seven patients (81 implants) completed the 5-year follow-up. The 5-year cumulative survival and success rates were 97.8%, and the mean MBL change from implant insertion to 5 years was − 0.80 ± 1.13 mm. Optimal papilla index scores were observed at 90.1% of sites at 5 years compared with 32.8% of sites at insertion. Pink esthetic score, modified bleeding and plaque indices, and OHIP showed statistically significant improvement at the 5-year follow-up. Conclusions Immediately provisionalized tapered conical connection implants promote marginal bone stability and excellent esthetic outcomes after 5 years of function. Clinical relevance This treatment is a viable option for patients requiring immediately provisionalized single-tooth restorations in the esthetic zone and shows favorable long-term clinical outcomes, including marginal bone stability and excellent esthetics.

Predicting the spread and persistence of genetically modified dominant sterile male mosquitoes

Background Reproductive containment provides an opportunity to implement a staged-release strategy for genetic control of malaria vectors, in particular allowing predictions about the spread and persistence of (self-limiting) sterile and male-biased strains to be compared to outcomes before moving to (self-sustaining) gene-drive strains. Methods In this study, we: (i) describe a diffusion–advection–reaction model of the spread and persistence of a single cohort of male mosquitoes; (ii) elicit informative prior distributions for model parameters, for wild-type (WT) and genetically modified dominant sterile strains (DSM); (iii) estimate posterior distributions for WT strains using data from published mark-recapture-release (MRR) experiments, with inference performed through the Delayed-Rejection Adaptive Metropolis algorithm; and (iv) weight prior distributions, in order to make predictions about genetically modified strains using Bayes factors calculated for the WT strains. Results If a single cohort of 5000 genetically modified dominant sterile male mosquitoes are released at the same location as previous MRR experiments with their WT counterparts, there is a 90% probability that the expected number of released mosquitoes will fall to < 1 in 10 days, and that by 12 days there will be a 99% probability that no mosquitoes will be found more than 150 m from the release location. Conclusions Spread and persistence models should form a key component of risk assessments of novel genetic control strategies for malaria vectors. Our predictions, used in an independent risk assessment, suggest that genetically modified sterile male mosquitoes will remain within the locality of the release site, and that they will persist for a very limited amount of time. Data gathered following the release of these mosquitoes will enable us to test the accuracy of these predictions and also provide a means to update parameter distributions for genetic strains in a coherent (Bayesian) framework. We anticipate this will provide additional insights about how to conduct probabilistic risk assessments of stage-released genetically modified mosquitoes. Graphical abstract

1282Causal inference in multi-cohort studies using the target trial approach

Focus of Presentation Utilising data from multiple cohorts to address causal questions in health research has become increasingly widespread due to a number of advantages. These include improved precision of estimates, in particular to investigate effect heterogeneity as well as rare events and exposures, and the ability to examine the replicability of findings. However, undertaking causal inference in multi-cohort studies also faces several challenges, which makes clear causal thinking even more important than in single-cohort studies. We propose the use of the “target trial” framework for the conduct of causal inference in multi-cohort studies. Findings Using two case studies, the first considering the effect of maternal mental health on emotional reactivity and the second examining the influence of exposure to adversity on inflammatory outcomes in childhood, we describe and demonstrate how the target trial approach enables clear definition of the target estimand and systematic consideration of sources of bias. Considering the target trial as the reference point allows the identification of potential biases within each study, so that analysis can be planned to reduce them. Furthermore, the interpretation of findings is assisted by an understanding of the unavoidable biases that may be compounded when pooling data from multiple cohorts, or that may explain discrepant findings across cohorts. Conclusions/Implications Use of the target trial framework in multi-cohort studies helps strengthen causal inferences through improved analysis design and clarity in the interpretation of findings. Key messages The target trial framework, already well-established for casual inference in single-cohort studies, is recommended for the conduct of causal inference in multi-cohort studies.

Self-Supervised Adversarial Distribution Regularization for Medication Recommendation

Medication recommendation is a significant healthcare application due to its promise in effectively prescribing medications. Avoiding fatal side effects related to Drug-Drug Interaction (DDI) is among the critical challenges. Most existing methods try to mitigate the problem by providing models with extra DDI knowledge, making models complicated. While treating all patients with different DDI properties as a single cohort would put forward strict requirements on models' generalization performance. In pursuit of a valuable model for a safe recommendation, we propose the Self-Supervised Adversarial Regularization Model for Medication Recommendation (SARMR). SARMR obtains the target distribution associated with safe medication combinations from raw patient records for adversarial regularization. In this way, the model can shape distributions of patient representations to achieve DDI reduction. To obtain accurate self-supervision information, SARMR models interactions between physicians and patients by building a key-value memory neural network and carrying out multi-hop reading to obtain contextual information for patient representations. SARMR outperforms all baseline methods in the experiment on a real-world clinical dataset. This model can achieve DDI reduction when considering the different number of DDI types, which demonstrates the robustness of adversarial regularization for safe medication recommendation.

Using MTurk to Capture Change: Tracking Perceptions of COVID-19 in a U.S. sample through the UO-EPIDeMIC Study

The University of Oregon’s “Emotions and Polarization in Decisions &amp; Media in COVID-19” (UO-EPIDeMIC) study tracked perceptions of the COVID-19 pandemic from February 17th of 2020 through December 17th of 2020 in a single cohort of 1,284 American Mechanical Turk (MTurk) workers. Affect, attitudes, risk perceptions, intentions for protective behaviors, trust in various actors, media usage, and attitudes towards potential solutions were measured throughout the pandemic’s progression. Through six longitudinal waves of surveys, we demonstrate Mturk’s potential as a tool for recruiting high-quality longitudinal cohorts of participants. The data collected through these surveys have been used in various lines of research to learn about how individuals react and adapt to a global catastrophe.

HPV and Cytology Testing in Women Undergoing 9-Valent HPV Opportunistic Vaccination: A Single-Cohort Follow Up Study

Background: This study evaluates the possible effect of 9-valent (9vHPV) vaccination on the results of HPV and cytological tests in a cohort of adult women. Methods: This study is a retrospective, single-cohort, monocentric study. Sexually active women aged 14–70 years, who underwent 9vHPV vaccination, were enrolled. Dose administration dates, side effects and data on Pap smears and HPV tests performed before and after the first vaccine dose were collected. Subjects were considered “unexposed” to the vaccine for all time intervals before the first dose administration, and “exposed” to the first, second and third vaccine doses in all time intervals following each specific dose. Results: A total of 512 women underwent the first 9vHPV dose administration and were enrolled in the study. Median age at vaccination was 30.5 (14–70). Log-rank tests and Cox regression analyses showed a highly statistically significant (p < 0.0001) difference in the time to negativization after the exposure to the third vaccine dose in the 207 women starting with a Pap+ smear (HR (95% C.I.), 2.66 (1.83–3.86)) and in the 198 women starting with an HPV HR+ test (HR (95% C.I.), 7.80 (4.83–12.60)). Conclusions: 9vHPV vaccination may play a role in shortening the clearance time of HPV HR+ or Pap positivity in sexually active adult women.