Academic literature on the topic 'Post-extraction complications'

The biometric changes of the human eye with lowered intraocular pressure (IOP), like those observed after glaucoma surgery, have been investigated by in vivo studies. Leydolt et al demonstrated IOP-dependent axial eye length changes in human eyes. An axial eye length decrease of 2 μm/mm Hg with short-term reduction of IOP was observed. They suggested that this concomitant shortening of axial length and IOP reduction could be a result of a decrease in scleral length owing to the reduced IOP or an increase in choroidal blood flow compensating the reduced ocular fundus pulsations during the increased IOP phase. In another in vivo study, Read et al showed that axial length underwent significant variation over a 24-hour period in normal human eyes, and a significant association existed between the change in axial length and the change in IOP, as measured by dynamic contour tonometry. The association observed between IOP and axial length was found to be consistent with the hypothesis of passive expansion and contraction of the globe in response to IOP. Studies on trabeculectomy patients, performed with or without cataract extraction, have shown a significant decrease of axial length after surgery, correlated with IOP reduction. Similarly, IOP lowering after tube shunt surgery may produce a decrease in axial length that is dependent on the amount of IOP lowering. Understanding how axial length changes impact clinical decisions, such as lens power choice for cataract surgery, is important for treating patients after glaucoma surgery or patients who have had previous glaucoma surgery. Table 41.1 summarizes reported axial length changes following different types of trabeculectomy procedures. Nemeth and Horoczi noted decreased axial length and increased thickness and volume of the ocular wall 4 days after trabeculectomy. In a retrospective study, Cashwell and Martin found a significant decrease in axial length (mean: 0.423 mm, range: 2.8 to +0.5 mm) after successful initial trabeculectomy in 62 patients using ultrasound biometry. Preoperative factors found to be associated with a greater decrease in axial length were young age, myopia, exposure to an antimetabolite, and a post-trabeculectomy IOP drop greater than 30 mmHg.

Contents. Principles of surgery of the mouth. Asepsis and antisepsis. Forceps, elevators, and other instruments. The extraction of teeth. Complications of extracting teeth. Post-operative bleeding. Suturing. Dento-alveolar surgery: bisphosphonates. Dento-alveolar surgery: removal of roots. Dento-alveolar surgery: removal of unerupted teeth. Dento-alveolar surgery: removal of third molars. Dento-alveolar surgery: coronectomy. Dento-alveolar surgery: third-molar technique. Dento-alveolar surgery: apicectomy. Dento-alveolar surgery: other aids to endodontics. Dento-alveolar surgery: helping the orthodontist. Dento-facial infections. Biopsy. Cryosurgery. Non-tumour soft-tissue lumps in the mouth. Non-tumour hard-tissue lumps. Cysts of the jaws. Benign tumours of the mouth. The maxillary antrum. Minor preprosthetic surgery. Implantology. Lasers. Minimally invasive surgery.

Endovascular repair is a minimal invasive alternative to open surgical therapy. From a long term perspective, complications such as prostheses displacement or leaks inside the aneurysm sac (endoleaks) could appear influencing the evolution of treatment. The objective of this work is to develop a preliminary Computer-aided diagnosis system (CAD) for an automated classification of EVAR progression from computed tomography angiography CTA images. The system is based on the extraction of texture features from thrombus aneurysm samples and a posterior classification. Regions of interest (ROIs) from patients with different post-EVAR evolution were extracted by experienced radiologists. Three conventional texture-analysis methods such as the gray level co-occurrence matrix (GLCM), the gray level run length matrix (GLRLM), and the gray level difference method (GLDM), were applied to each ROI to obtain texture features. Classification of the ROI is carried out by three different strategies. In the first one each feature set is fed to a neural network (NN). The second consists of a single neural network fed with a reduced version of texture features after a feature selection process. The third one comprised an ensembles of classifiers (ECs), formed by three NNs, each using as input one of the feature sets. The final decision is based on the application of a voting scheme across the outputs of the individual NNs. Classification results from the three classification strategies are evaluated using a receiver operating-characteristics (ROC) analysis and area under the roc curve (Az) performance. The multiple classification scheme using the three sets of texture features results in a better performance, as compared to the classification performance of the other alternatives.

Because of the application of vitreoretinal surgical techniques to a broader range of posterior segment diseases and because cataract develops frequently in postvitrectomy eyes, cataract surgeons should be familiar with the challenges of cataract extraction in vitrectomized eyes. Cataract surgery after pars plana vitrectomy significantly improves visual acuity in 85% of cases, limited by retinal comorbidity and surgical complications. However, despite recent advances, this surgery remains a special challenge. The cataract surgeon can prepare for these challenges with awareness of such potential factors as an excessively mobile posterior capsule, silicon oil removal and special considerations concerning intraocular lens selection and power calculation. And consider the postoperative complications as posterior capsule opacification or refractive errors.

This research addresses the challenges associated with data-driven soft sensors in industrial applications, where successful implementations remain limited. The scarcity of practical applications can be attributed to variable operating conditions and frequent disturbances in real-time processes. Industrial data are often nonlinear, dynamic, and highly unbalanced, complicating efforts to capture the essential characteristics of underlying processes. To tackle these issues, we propose a comprehensive solution for industrial application, that encompasses feature selection, feature extraction, and model updating. Feature selection aims to pinpoint the independent variables that have a substantial impact on key performance indicators, including quality, safety, efficiency, reliability, and sustainability. By doing so, it simplifies the model and boosts its predictive accuracy. The process begins with screening variables based on process knowledge, followed by a thorough analysis of correlation and redundancy to eliminate redundant information, which can burden computational resources and degrade prediction accuracy. We propose a mutual information-based algorithm for feature selection that assesses the relevance and redundancy among process variables through a comprehensive correlation function. This algorithm ranks variables by their importance using a Greedy search method to identify the optimal set of variables. After selecting the optimal variables, feature extraction is carried out to derive internal features from this set and establish a relationship between these latent features and the output variables. Given the intricate nature of industrial processes, we employ deep learning techniques, specifically Long Short-Term Memory (LSTM) networks, which are a type of Recurrent Neural Network (RNN) well-suited for capturing long-term dependencies in sequential data. LSTMs excel at modeling temporal correlations due to their ability to maintain memory states that allow for learning from sequential data over extended periods. To address short-term nonstationary features resulting from process disturbances, we incorporate a differential unit into the latent layer of the LSTM network. Once trained, the model is updated during online applications to incorporate gradual changes in equipment and reaction agents. Quality-related data, although typically available only post-measurement, can be leveraged to fine-tune model parameters, ensuring sustained predictive accuracy over time. To validate our approach, we present a case study on a delayed coker unit, yielding promising long-term predictions for tube metal temperature and showcasing the potential of our methodology for industrial applications.

Objectives. To summarize the evidence regarding diagnosis, prophylactic treatment, symptomatic treatment, and repeat surgery of tethered spinal cord. Data sources. We searched PubMed®, Embase®, CINAHL, Web of Science, SCOPUS, clinicaltrials.gov, ICTRP, Cochrane Database of Systematic Reviews, PROSPERO, ECRI repository, G-I-N, MagicApp, and ClinicalKey from inception to March 2024; reference-mined reviews; and contacted research authors. Review methods. The review followed a detailed protocol and was supported by a Technical Expert Panel. Systematic review software (DistillerSR) was utilized for all screening and data extraction tasks. Citation screening was facilitated by machine learning; two independent reviewers each screened full text citations for eligibility; one literature reviewer extracted data and a methodologist checked for accuracy. Risk of bias assessments focused on key sources of bias for diagnostic and intervention studies. We conducted strength of evidence (SoE) and applicability assessments for key outcomes. The protocol for the review has been registered in PROSPERO (CRD42023461296). Results. Searches identified 6,285 citations; 2,005 were obtained as full text. In total, 103 studies met inclusion criteria, with an additional 355 case series providing additional information. We found the strongest evidence for accuracy of MRI in diagnosing tethered spinal cord. Specifically, studies indicated this modality has medium to high diagnostic sensitivity and specificity (moderate SoE). A small number of existing studies suggested benefits of prophylactic surgery, but it was also associated with complications such as surgical site infection (low SoE). A larger body of evidence evaluated various treatments for symptomatic patients, with the majority focused on surgical detethering. Studies reported improvement of neurological status after surgical detethering (low SoE), but it was also associated with post-operative complications such as cerebrospinal fluid leakage (moderate SoE). A very small body of evidence exists for revision detethering and spinal column shortening for repeat surgery (low or insufficient SoE for all outcomes). Across diagnosis, prophylactic treatment, symptomatic treatment, and repeat surgery there was insufficient evidence for multiple key outcomes (e.g., over- or undertreatment, clinical impact of diagnostic modalities, ambulation or quality of life outcomes) and thus no evidence statements could be derived. Conclusions. The evidence base for the diagnosis and treatment of tethered spinal cord is limited, with few exceptions (use of MRI or ultrasound for diagnosis, surgical detethering improving neurological status in symptomatic patients, complications associated with open detethering surgery) and would benefit from stronger study designs that include tool evaluations reporting diagnostic performance and treatment studies with concurrent comparator.