Quiet eye (QE), the visual fixation on a target before initiation of a critical action, is associated with improved performance. While QE is trainable, it is unclear whether QE can directly predict performance, which has implications for training interventions. This study predicted basketball shot outcome (make or miss) from visuomotor control variables using a decision tree classification approach. Twelve basketball athletes completed 200 shots from six on-court locations while wearing mobile eye-tracking glasses. Training and testing data sets were used for modeling eight predictors (shot location, arm extension time, and absolute and relative QE onset, offset, and duration) via standard and conditional inference decision trees and random forests. On average, the trees predicted over 66% of makes and over 50% of misses. The main predictor, relative QE duration, indicated success for durations over 18.4% (range: 14.5%-22.0%). Training to prolong QE duration beyond 18% may enhance shot success.

OBJECTIVE: This study aims to determine whether machine learning can identify specific combinations of long-term conditions (LTC) associated with increased sarcopenia risk and hence address an important evidence gap-people with multiple LTC (MLTC) have increased risk of sarcopenia but it has not yet been established whether this is driven by specific combinations of LTC.
METHODS: Decision trees were used to identify combinations of LTC associated with increased sarcopenia risk. Participants were classified as being at risk of sarcopenia based on maximum grip strength of <32 kg for men and <19 kg for women. The combinations identified were triangulated with logistic regression.
METHODS: UK Biobank.
METHODS: UK Biobank participants with MLTC (two or more LTC) at baseline.
RESULTS: Of 140 001 participants with MLTC (55.3% women, median age 61 years), 21.0% were at risk of sarcopenia. Decision trees identified several LTC combinations associated with an increased risk of sarcopenia. These included drug/alcohol misuse and osteoarthritis, and connective tissue disease and osteoporosis in men, which showed the relative excess risk of interaction of 3.91 (95% CI 1.71 to 7.51) and 2.27 (95% CI 0.02 to 5.91), respectively, in age-adjusted models.
CONCLUSIONS: Knowledge of LTC combinations associated with increased sarcopenia risk could aid the identification of individuals for targeted interventions, recruitment of participants to sarcopenia studies and contribute to the understanding of the aetiology of sarcopenia.

OBJECTIVE: Preoperative and intraoperative diagnostic tools influence the surgical management of primary hyperparathyroidism (PHPT), whereby their performance of classification varies considerably for the two common causes of PHPT: solitary adenomas and multiglandular disease. A consensus on the use of such diagnostic tools for optimal perioperative management of all PHPT patients has not been reached.
METHODS: A decision tree model was constructed to estimate and compare the clinical outcomes and the cost-effectiveness of preoperative imaging modalities and intraoperative parathyroid hormone (ioPTH) monitoring criteria in a 21-year time horizon with a 3% discount rate. The robustness of the model was assessed by conducting a one-way sensitivity analysis and probabilistic uncertainty analysis.
METHODS: The US healthcare system.
METHODS: A hypothetical population consisting of 5000 patients with sporadic, symptomatic or asymptomatic PHPT.
METHODS: Preoperative and intraoperative diagnostic modalities for parathyroidectomy.
METHODS: Costs, quality-adjusted life-years (QALYs), net monetary benefits (NMBs) and clinical outcomes.
RESULTS: In the base-case analysis, four-dimensional (4D) CT was the least expensive strategy with US$10 276 and 15.333 QALYs. Ultrasound and 99mTc-Sestamibi single-photon-emission CT/CT were both dominated strategies while 18F-fluorocholine positron emission tomography was cost-effective with an NMB of US$416 considering a willingness to pay a threshold of US$95 958. The application of ioPTH monitoring with the Vienna criterion decreased the rate of reoperations from 10.50 to 0.58 per 1000 patients compared to not using ioPTH monitoring. Due to an increased rate of bilateral neck explorations from 257.45 to 347.45 per 1000 patients, it was not cost-effective.
CONCLUSIONS: 4D-CT is the most cost-effective modality for the preoperative localisation of solitary parathyroid adenomas and multiglandular disease. The use of ioPTH monitoring is not cost-effective, but to minimise clinical complications, the Miami criterion should be applied for suspected solitary adenomas and the Vienna criterion for multiglandular disease.

Aberrant methylation patterns in human DNA have great potential for the discovery of novel diagnostic and disease progression biomarkers. In this paper we used machine learning algorithms to identify promising methylation sites for diagnosing cancerous tissue and to classify patients based on methylation values at these sites. We used genome-wide DNA methylation patterns from both cancerous and normal tissue samples, obtained from the Genomic Data Commons consortium and trialled our methods on three types of urological cancer. A decision tree was used to identify the methylation sites most useful for diagnosis. The identified locations were then used to train a neural network to classify samples as either cancerous or non-cancerous. Using this two-step approach we found strong indicative biomarker panels for each of the three cancer types. These methods could likely be translated to other cancers and improved by using non-invasive liquid methods such as blood instead of biopsy tissue.

We here introduce Ensemble Optimizer (EnOpt), a machine-learning tool to improve the accuracy and interpretability of ensemble virtual screening (VS). Ensemble VS is an established method for predicting protein/small-molecule (ligand) binding. Unlike traditional VS, which focuses on a single protein conformation, ensemble VS better accounts for protein flexibility by predicting binding to multiple protein conformations. Each compound is thus associated with a spectrum of scores (one score per protein conformation) rather than a single score. To effectively rank and prioritize the molecules for further evaluation (including experimental testing), researchers must select which protein conformations to consider and how best to map each compound\'s spectrum of scores to a single value, decisions that are system-specific. EnOpt uses machine learning to address these challenges. We perform benchmark VS to show that for many systems, EnOpt ranking distinguishes active compounds from inactive or decoy molecules more effectively than traditional ensemble VS methods. To encourage broad adoption, we release EnOpt free of charge under the terms of the MIT license.

BACKGROUND: Palatal groove represents a relatively uncommon developmental root anomaly, usually found on the palatal aspect of maxillary incisors. While its origin is controversial, its presence predisposes to severe periodontal defects.
OBJECTIVE: This study aimed to provide a systematic review of the literature focusing on the varied diagnostic techniques and treatment modalities for periodontal lesions arising from the presence of palatal groove. Based on the existing evidence and knowledge, the study also provides a comprehensive decisional tree, guiding clinicians in the challenging decision-making process face to a palatal groove.
METHODS: The literature search was conducted on Medline and Cochrane databases by two independent reviewers, who also performed the screening and selection process, looking for English written articles reporting on diagnosis and management (all treatment approaches) of periodontal lesion(s) associated with a palatal groove. Based on this literature, a comprehensive decisional tree, including a standardized palatal groove evaluation and tailored treatment approaches, is proposed. Moreover, a clinical case is described to demonstrate the practical application of the developed decisional tree.
RESULTS: Over a total of 451 articles initially identified, 34 were selected, describing 40 patients with 40 periodontal lesions associated with palatal grooves. The case report illustrates a deep, large, circumferential intra-bony defect on the palatal side of the tooth #22 associated with a shallow, moderately long palatal groove in an 18-year-old male patient. Following reevaluation, a single flap surgery was deemed necessary, combined with a regenerative procedure. At 2 years post-treatment, the tooth #22 is healthy, in a functional and esthetic position. The decision-making process, based on local and systemic patient\'s conditions, should allow an early and precise diagnosis to prevent further complications and undertake an adequate treatment.
CONCLUSIONS: Palatal grooves are relatively rare; however, they are frequently associated with severe periodontal defects. The identification, diagnosis, prompt, and tailored management of the associated lesion is essential to mitigate potential periodontal and endodontic complications related to the presence of palatal groove.
BACKGROUND: [ https://www.crd.york.ac.uk/prospero/ ], identifier [C CRD42022363194].

Obesity is an abnormal and potentially dangerous condition caused by excess body fat accumulation. The number of people with obesity is increasing worldwide. Obesity is the primary cause of various diseases; therefore, it is crucial to make efforts to control body weight. Identifying the factors that influence men with obesity to attempt to control and not control their weight is essential. The objective of this study was to create a prediction model for weight control experience among Korean men in their 30 s and 40 s. We analyzed data from the 2022 Community Health Survey and included 12,311 men who were overweight or obese. The men were divided into two groups based on their weight control experience: (1) Yes group (n = 9405) and (2) No group (n = 2906). Chi-square and independent t-tests were used to compare general and health-related characteristics between the groups. Decision tree analysis was used to build a prediction model for weight control experience. A split-sample test was conducted to validate the model. From the results of this study, various models predicting weight control experience were derived. From the decision tree model without setting the first node, those who weighed below average, had a high school diploma or less, and did not know their blood sugar levels had the highest probability of not controlling their weight at 55.3%. In the prediction model where the first node was set to age, those in their 40 s who thought their weight was below average and were unaware of their blood sugar levels had the highest rate of not trying to control their weight at 50.1%. In the prediction model where the first node was set to BMI, those who were overweight but thought their weight was below average and had a high school diploma or less had the highest rate of not trying to control their weight at 51.5%. There is an urgent need to provide obesity prevention and management education to those who have no weight control experience, particularly those at high risk, as identified in this study.

A stroke is a dangerous, life-threatening disease that mostly affects people over 65, but an unhealthy diet is also contributing to the development of strokes at younger ages. Strokes can be treated successfully if they are identified early enough, and suitable therapies are available. The purpose of this study is to develop a stroke prediction model that will improve stroke prediction effectiveness as well as accuracy. Predicting whether someone is suffering from a stroke or not can be accomplished with this proposed machine learning algorithm. In this research, various machine learning techniques are evaluated for predicting stroke on the healthcare stroke dataset. The feature selection algorithms used here are gradient boosting and random forest, and classifiers include the decision tree classifier, Support Vector Machine (SVM) classifier, logistic regression classifier, gradient boosting classifier, random forest classifier, K neighbors classifier, and Xtreme gradient boosting classifier. In this process, different machine-learning approaches are employed to test predictive methods on different data samples. As a result obtained from the different methods applied, and the comparison of different classification models, the random forest model offers an accuracy rate of 98%.

BACKGROUND: Older patients with diabetic kidney disease (DKD) often do not receive optimal pharmacological treatment. Current clinical practice guidelines (CPGs) do not incorporate the concept of personalised care. Clinical decision support (CDS) algorithms that consider both evidence and personalised care to improve patient outcomes can improve the care of older adults. The aim of this research is to design and validate a CDS algorithm for prescribing renin-angiotensin-aldosterone system inhibitors (RAASi) for older patients with diabetes.
METHODS: The design of the CDS tool included the following phases: (1) gathering evidence from systematic reviews and meta-analyses of randomised clinical trials to determine the number needed to treat (NNT) and time-to-benefit (TTB) values applicable to our target population for use in the algorithm. (2) Building a list of potential cases that addressed different prescribing scenarios (starting, adding or switching to RAASi). (3) Reviewing relevant guidelines and extracting all recommendations related to prescribing RAASi for DKD. (4) Matching NNT and TTB with specific clinical cases. (5) Validating the CDS algorithm using Delphi technique.
RESULTS: We created a CDS algorithm that covered 15 possible scenarios and we generated 36 personalised and nine general recommendations based on the calculated and matched NNT and TTB values and considering the patient\'s life expectancy and functional capacity. The algorithm was validated by experts in three rounds of Delphi study.
CONCLUSIONS: We designed an evidence-informed CDS algorithm that integrates considerations often overlooked in CPGs. The next steps include testing the CDS algorithm in a clinical trial.

Technology plays a role in nearly every aspect of healthcare delivery. Health systems must continually invest in new and existing technology and analytics platforms to scale initiatives, enable innovation, and achieve interoperability to meet the needs and expectations of patients and clinicians while remaining focused on the organization\'s mission and strategic priorities. In this process, decision-makers must determine how to allocate technological resources to platforms that meet clinical and administrative needs while reducing the need for frequent replacement or reconfiguration. Advances in artificial intelligence and its capabilities add urgency and complexity to technology investment decisions. An important consideration during this process is when to build new technology infrastructure and when to partner with existing companies and buy technology solutions. This case study explores a major academic medical center\'s approach to that decision, including the factors that influenced it and the outcomes of two solutions that were developed in-house.