Publications by authors named "Zhuqi Miao"

Objective: Develop a novel technique to identify an optimal number of regression units corresponding to a single risk point, while creating risk scoring systems from logistic regression-based disease predictive models. The optimal value of this hyperparameter balances simplicity and accuracy, yielding risk scores of small scale and high accuracy for patient risk stratification.

Materials And Methods: The proposed technique applies an adapted line search across all potential hyperparameter values.

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Spin is a way of reporting that distorts the true findings; we sought to investigate the prevalence of spin in systematic review abstracts on psoriasis treatments and whether study characteristics were associated with spin. We searched MEDLINE and Embase to obtain our sample. Screening and data extraction were performed in a masked duplicate fashion.

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With the increasing availability of electronic health records (EHR), significant progress has been made on developing predictive inference and algorithms by health data analysts and researchers. However, the EHR data are notoriously noisy due to missing and inaccurate inputs despite the information is abundant. One serious problem is that only a small portion of patients in the database has confirmatory diagnoses while many other patients remain undiagnosed because they did not comply with the recommended examinations.

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Background: Spin-the misrepresentation of a study's actual results-has the potential to alter a clinician's interpretation of the study's findings and therefore could affect patient care. Studies have shown spin frequently occurs in abstracts of systematic reviews from a variety of other medical disorders and specialties.

Aims: Our primary aim was to evaluate whether the nine most severe types of spin occurred in systematic review abstracts' concerning diabetic neuropathy treatments.

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Introduction: Spin - the beautification of study results to emphasise benefits or minimise harms - is a deceptive reporting strategy with the potential to affect clinical decision-making adversely. Few studies have investigated the extent of spin in systematic reviews. Here, we sought to address this gap by evaluating the presence of the nine most severe forms of spin in the abstracts of systematic reviews on treatments for postoperative nausea and vomiting (PONV).

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Available literature clearly indicates that successful implementation of telemedicine and telehealth has been a challenge. This challenge is further amplified if the reader must consider this implementation in a rural setting. In this article the authors discuss some of the key challenges associated with this implementation.

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Background: Approximately 18 in every 100 000 people have experienced a ruptured Achilles tendon. Despite the prevalence of this condition, treatment options remain contested.

Hypothesis/purpose: The objective of this study was to evaluate the use of spin-reporting practices that may exaggerate benefit or minimize harm-in abstracts of systematic reviews related to Achilles tendon repair.

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Substance abuse or drug dependence is a prevalent phenomenon, and is on the rise in United States. Important contributing factors for the prevalence are the addictive nature of certain medicinal/prescriptive drugs, individual dispositions (biological, physiological, and psychological), and other external influences (e.g.

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Aims: Currently, there is a growing body of research demonstrating that spin - the misinterpretation and distortion of a study's findings - is common in different fields of medicine. To our knowledge, no study has investigated its presence in systematic reviews focused on diabetic therapies.

Methods: We performed a cross-sectional study by searching MEDLINE and Embase for systematic reviews focused on pharmacologic treatments for type 2 diabetes mellitus.

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Diabetic retinopathy (DR) is a leading cause for blindness among working-aged adults. The growing prevalence of diabetes urges for cost-effective tools to improve the compliance of eye examinations for early detection of DR. The objective of this research is to identify essential predictors and develop predictive technologies for DR using electronic health records.

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Article Synopsis
  • This study investigates the presence of "spin," or biased reporting of findings, in the abstracts of systematic reviews related to breast cancer, highlighting its prevalence and potential effects on clinical decision-making.
  • Out of 11,717 articles screened, 581 consisted of studies meeting inclusion criteria, and 21% of those exhibited at least one type of spin, particularly in pharmacological and surgical interventions.
  • The findings indicate that systematic reviews on breast cancer could mislead readers due to distorted presentations of research outcomes, underscoring a need for improved reporting standards in this area.
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Objective: Our primary objective was to identify the prevalence of spin - misleading reporting practices that overemphasize benefit or underemphasize harm - within the abstracts of systematic reviews and meta-analyses focused on surgical management of osteoarthritis of the knee.

Methods: A search string was developed to search Ovid MEDLINE and Ovid Embase for articles pertaining to surgical management, or quality of life after surgical management, of osteoarthritis of the knee. Titles and abstracts were screened according to our protocol, developed , followed by full-text evaluation for spin in included articles.

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Background: The purpose of this study was to evaluate known and yet unknown risk factors associated with readmission to the hospital within 30 days after hip fracture.

Methods: In this study, we used the Cerner Health Facts Electronic Health Record database data from January to August 2015. The univariate association of each variable (discharge location, demographic details, and comorbidities) against the 30-day readmission status was evaluated using the Chi-square test or the Fisher exact test.

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Background: High-throughput sequencing technology has revolutionized both medical and biological research by generating exceedingly large numbers of genetic variants. The resulting datasets share a number of common characteristics that might lead to poor generalization capacity. Concerns include noise accumulated due to the large number of predictors, sparse information regarding the p≫n problem, and overfitting and model mis-identification resulting from spurious collinearity.

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