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Published in Ken Addley, MCQs, MEQs and OSPEs in Occupational Medicine, 2023
The methods for the review, including how the quality of studies is assessed, should be agreed before the study commences, and should be published in the methods. The diamond represents the pooled results of the included studies. Clinical heterogeneity reflects varied methods such as selection of patients and range of interventions. Statistical heterogeneity reflects odds ratios amongst the studies—some indicate harm from the intervention, some benefit. It can be assessed using a Chi-squared test. A funnel plot is a graphical representation that illustrates whether there may be publication bias when selecting the studies for the analysis. A search strategy should include more than one database and include an adequate range of keywords and not just one language.
Axial Spondyloarthritis
Published in Jason Liebowitz, Philip Seo, David Hellmann, Michael Zeide, Clinical Innovation in Rheumatology, 2023
A number of efforts are underway to improve time from symptom onset to diagnosis. This has included screening efforts, such as use of questionnaires among patients at high risk (i.e., those with IBD or uveitis), primary care–based questionnaires for patients with back pain, postings in public transportation systems to encourage patients with symptoms to come in for evaluation, and use of administrative or electronic medical records to build screening algorithms.20–25 One challenge with these individual efforts is that it seems they may need to be tailored for each individual population because of the heterogeneity of the disease and cultural and health care system differences across the world.26
Medications
Published in Henry J. Woodford, Essential Geriatrics, 2022
Meta-analysis is a technique for combining several clinical trials. Its value depends on the quality of the included study data. Adverse effects tend to not be reported or detected in standardised ways, which makes them harder to combine. Heterogeneity refers to differences between studies that is not explained by chance alone, e.g. due to methodological differences or the types of intervention or participants used.22 This can be tested statistically (e.g. Higgins' I2 value of 50% or more suggests heterogeneity).
Association between asthma and caries-related salivary factors: a meta-analysis
Published in Journal of Asthma, 2022
Ömer Hatipoğlu, Fatma Pertek Hatipoğlu
Variabilities in the study design and risk of bias were examined to estimate the methodological heterogeneity. Clinical heterogeneity was assessed by examining the inconsistencies among cases, controls, and study outcomes. Statistical heterogeneity was assessed with the chi-square, tau-square, and Higgins I2 tests. Statistical heterogeneity among studies was assessed using the I2 statistic and categorized as not significant (<30%), moderate (30–50%), substantial (50–75%), or considerable (75–100%) (11). Even if statistical homogeneity is detected in a model, the use of a random-effects model is recommended over a fixed-effects model if there is heterogeneity between the study populations (12). Since methodological, clinical, and statistical homogeneity were not achieved, a random-effects model with 95% CI was chosen as the meta-analysis model.
Association of Endothelial Nitric Oxide Synthase 894G > T Polymorphism with Preeclampsia Risk: A Systematic Review and Meta-Analysis based on 35 Studies
Published in Fetal and Pediatric Pathology, 2021
Hajar Abbasi, Seyed Alireza Dastgheib, Amaneh Hadadan, Mojgan Karimi-Zarchi, Atiyeh Javaheri, Bahare Meibodi, Leila Zanbagh, Razieh Sadat Tabatabaei, Hossein Neamatzadeh
Heterogeneity plays an important role when performing meta-analysis and finding the source of heterogeneity is very important for the final result of meta-analysis [56–58]. The heterogeneity might be caused by the differences in the selection of controls, ethnicity, environmental exposures, genotyping methods, sample size, age distribution, and lifestyle factors [59, 60]. In the current meta-analysis, there was evidence of statistical heterogeneity between the analyses of eNOS 894 G > T polymorphism and preeclampsia under all five genetic models in overall estimations. Results of meta-regression demonstrate that ethnicity of the studied population is a source of the heterogeneity. Moreover, studies of low quality might have increased the between-study heterogeneity and led to misleading results. Thus, we performed sensitivity analysis by excluding HWE-violating studies and confirmed the robustness of our findings.
Collaborative networks to achieve innovations in care
Published in The Journal of Spinal Cord Medicine, 2021
B. Catharine Craven, Kristin Musselman, Suzanne Humphreys, Kristen Walden, Jessica Parsons, Jessica Eapen, Vanessa K Noonan, Christiana L Cheng, Charlene Yousefi, John Chernesky, Élizabeth Côté-Boileau, Nadine Ibrahim, Anifa Luyinga Kalay, Darryl Kingston, Louise Clément, M. Bayley, A. Kua, E. Patsakos, C. Cheng, J. Eng, C. Ho, M. Queree, Farnoosh Farahani, Heather Flett, Carol Scovil, Ivie Evbuomwan, Peter Athanasopoulos, Dalton Wolf, Sophie Ebsary, Christopher McBride, Bill Adair, Nancy Beaton, Michael Bury, Darlene Cooper, Shaun Dyer, Stuart Howe, Launel Scott, Alan Stanley
In Canada, a data strategy is being developed by engaging a broad range of stakeholders to enhance how SCI data, collected from RHSCIR and other data sources, can inform both research and care. This will involve further embedding SCI core data elements into standards of care to support quality improvement initiatives and aligning with the vision of a learning health system defined as a process where advances in science, informatics, and care generate new knowledge, which is refined and informs best practices as part of continuous health care improvement.11 Given the heterogeneity of SCI, another focus will be to link clinical data and patient-reported outcomes to other SCI data types, including imaging and biological data (e.g. neurochemical biomarkers) to develop personalized treatment algorithms. A key part of the data strategy will involve engagement of patients, families, and community organizations to ensure the SCI data collected is meaningful and ultimately can support optimizing outcomes following SCI.