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PubMed Publications| Keyword search (4,163 papers available) |  |
"bias" Keyword-tagged Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | MATES: A tool for appraising the completeness with which a meta-analysis has been reported | Morrison K; Pottier P; Pollo P; Ricolfi L; Williams C; Yang Y; Beillouin D; Cardoso SJ; Ferreira V; Gallagher B; Gan JL; Hao G; Keikha M; Kozlowsky-Suzuki B; Kiran Kumara TM; Latterini F; Leverkus AB; Macartney EL; Manrique SM; Martinig AR; Mizuno A; Nanayakkara S; Ntzani E; Ouédraogo DY; Pursell E; Simpson Z; Sleight H; Woon KS; Xia Z; Ghannad M; Grames E; Hennessy EA; IntHout J; Moher D; O' Dea RE; Page MJ; Whaley P; Lagisz M; Nakagawa S; | 41411971 BIOLOGY |
| 2 | Weight bias, stigma and discrimination: a call for greater conceptual clarity | Côté M; Forouhar V; Sacco S; Baillot A; Himmelstein M; Hussey B; Incollingo Rodriguez AC; Nagpal TS; Nutter S; Patton I; Pearl RL; Puhl RM; Ramos Salas X; Russell-Mayhew S; Alberga AS; | 41280193 HKAP |
| 3 | Unintended consequences of measuring gestational weight gain: how to reduce weight stigma in perinatal care | Alberga AS; Incollingo Rodriguez AC; Nagpal TS; | 40652172 HKAP |
| 4 | The β2-adrenergic biased agonist nebivolol inhibits the development of Th17 and the response of memory Th17 cells in an NF-κB-dependent manner | Hajiaghayi M; Gholizadeh F; Han E; Little SR; Rahbari N; Ardila I; Lopez Naranjo C; Tehranimeh K; Shih SCC; Darlington PJ; | 39445009 BIOLOGY |
| 5 | Weight bias among Canadians: Associations with sociodemographics, BMI and body image constructs | Côté M; Forouhar V; Edache IY; Alberga AS; | 38964079 HKAP |
| 6 | Exploring the association between internalized weight bias and mental health among Canadian adolescents | Lucibello KM; Goldfield GS; Alberga AS; Leatherdale ST; Patte KA; | 38676448 HKAP |
| 7 | Weighty words: exploring terminology about weight among samples of physicians, obesity specialists, and the general public | Wilson OWA; Nutter S; Russell-Mayhew S; Ellard JH; Alberga AS; MacInnis CC; | 38131299 HKAP |
| 8 | Putting things right: An experimental investigation of memory biases related to symmetry, ordering and arranging behaviour | Radomsky AS; Ouellet-Courtois C; Golden E; Senn JM; Parrish CL; | 37793286 PSYCHOLOGY |
| 9 | Do trauma cue exposure and/or PTSD symptom severity intensify selective approach bias toward cannabis cues in regular cannabis users with trauma histories? | DeGrace S; Romero-Sanchiz P; Tibbo P; Barrett S; Arenella P; Cosman T; Atasoy P; Cousijn J; Wiers R; Keough MT; Yakovenko I; O' Connor R; Wardell J; Rudnick A; Nicholas Carleton R; Heber A; Stewart SH; | 37625353 PSYCHOLOGY |
| 10 | Weight bias internalization and beliefs about the causes of obesity among the Canadian public | Vida Forouhar | 37620795 HKAP |
| 11 | Modeling venous bias in resting state functional MRI metrics | Huck J; Jäger AT; Schneider U; Grahl S; Fan AP; Tardif C; Villringer A; Bazin PL; Steele CJ; Gauthier CJ; | 37498014 PERFORM |
| 12 | Visual biases in evaluation of speakers' and singers' voice type by cis and trans listeners | Marchand Knight J; Sares AG; Deroche MLD; | 37205083 PSYCHOLOGY |
| 13 | Predictors of support for anti-weight discrimination policies among Canadian adults | Levy M; Forouhar V; Edache IY; Alberga AS; | 37139379 HKAP |
| 14 | How well do covariates perform when adjusting for sampling bias in online COVID-19 research? Insights from multiverse analyses | Joyal-Desmarais K; Stojanovic J; Kennedy EB; Enticott JC; Boucher VG; Vo H; Košir U; Lavoie KL; Bacon SL; | 36335560 HKAP |
| 15 | Recommendations for making editorial boards diverse and inclusive | Mahdjoub H; Maas B; Nuñez MA; Khelifa R; | 36280401 BIOLOGY |
| 16 | Exploring weight bias internalization in pregnancy | Nagpal TS; Salas XR; Vallis M; Piccinini-Vallis H; Alberga AS; Bell RC; da Silva DF; Davenport MH; Gaudet L; Rodriguez ACI; Liu RH; Myre M; Nerenberg K; Nutter S; Russell-Mayhew S; Souza SCS; Vilhan C; Adamo KB; | 35906530 HKAP |
| 17 | Sample size and precision of estimates in studies of depression screening tool accuracy: A meta-research review of studies published in 2018-2021 | Nassar EL; Levis B; Neyer MA; Rice DB; Booij L; Benedetti A; Thombs BD; | 35362161 PSYCHOLOGY |
| 18 | Inclusion of currently diagnosed or treated individuals in studies of depression screening tool accuracy: a meta-research review of studies published in 2018-2021 | Nassar EL; Levis B; Rice DB; Booij L; Benedetti A; Thombs BD; | 35334411 PSYCHOLOGY |
| 19 | The relationship between weight bias internalization and healthy and unhealthy weight control behaviours | Levy M; Kakinami L; Alberga AS; | 35201546 PERFORM |
| 20 | Mapping changes in the obesity stigma discourse through Obesity Canada: a content analysis | Kirk SF; Forhan M; Yusuf J; Chance A; Burke K; Blinn N; Quirke S; Salas XR; Alberga A; Russell-Mayhew S; | 35071667 HKAP |
| 21 | Vaccine hesitancy: evidence from an adverse events following immunization database, and the role of cognitive biases | Azarpanah H; Farhadloo M; Vahidov R; Pilote L; | 34530804 JMSB |
| 22 | Data-driven methods distort optimal cutoffs and accuracy estimates of depression screening tools: a simulation study using individual participant data | Bhandari PM; Levis B; Neupane D; Patten SB; Shrier I; Thombs BD; Benedetti A; | 33838273 CONCORDIA |
| 23 | Weight bias and support of public health policies | Edache IY; Kakinami L; Alberga AS; | 33990876 PERFORM |
| 24 | Predicting Interpersonal Outcomes From Information Processing Tasks Using Personally Relevant and Generic Stimuli: A Methodology Study | Serravalle L; Tsekova V; Ellenbogen MA; | 33071861 CRDH |
| 25 | Prediction Errors in Depression: A Quasi-Experimental Analysis. | Radomsky AS, Wong SF, Dussault D, Gilchrist PT, Tesolin SB | 32746394 PSYCHOLOGY |
| 26 | The Association Between Weight-Based Teasing from Peers and Family in Childhood and Depressive Symptoms in Childhood and Adulthood: A Systematic Review. | Szwimer E, Mougharbel F, Goldfield GS, Alberga AS | 32002762 HKAP |
| 27 | Group sample sizes in nonregulated health care intervention trials described as randomized controlled trials were overly similar | Thombs BD; Levis AW; Azar M; Saadat N; Riehm KE; Sanchez TA; Chiovitti MJ; Rice DB; Levis B; Fedoruk C; Lyubenova A; Malo Vázquez de Lara AL; Kloda LA; Benedetti A; Shrier I; Platt RW; Kimmelman J; | 31866472 LIBRARY |
| 28 | Computer-Aided Diagnosis System of Alzheimer's Disease Based on Multimodal Fusion: Tissue Quantification Based on the Hybrid Fuzzy-Genetic-Possibilistic Model and Discriminative Classification Based on the SVDD Model. | Lazli L, Boukadoum M, Ait Mohamed O | 31652635 ENCS |
| 29 | Dopamine and light: effects on facial emotion recognition. | Cawley E, Tippler M, Coupland NJ, Benkelfat C, Boivin DB, Aan Het Rot M, Leyton M | 28633582 CSBN |
| 30 | Investigation of the confounding effects of vasculature and metabolism on computational anatomy studies. | Tardif CL, Steele CJ, Lampe L, Bazin PL, Ragert P, Villringer A, Gauthier CJ | 28159689 PERFORM |
| Title: | MATES: A tool for appraising the completeness with which a meta-analysis has been reported | ||||
| Authors: | Morrison K, Pottier P, Pollo P, Ricolfi L, Williams C, Yang Y, Beillouin D, Cardoso SJ, Ferreira V, Gallagher B, Gan JL, Hao G, Keikha M, Kozlowsky-Suzuki B, Kiran Kumara TM, Latterini F, Leverkus AB, Macartney EL, Manrique SM, Martinig AR, Mizuno A, Nanayakkara S, Ntzani E, Ouédraogo DY, Pursell E, Simpson Z, Sleight H, Woon KS, Xia Z, Ghannad M, Grames E, Hennessy EA, IntHout J, Moher D, O', Dea RE, Page MJ, Whaley P, Lagisz M, Nakagawa S | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/41411971/ | ||||
| DOI: | 10.1016/j.envint.2025.109935 | ||||
| Publication: | Environment international | ||||
| Keywords: | Reliability; Reproducibility; Risk of bias; Transparency; Triage; | ||||
| PMID: | 41411971 | Category: | Date Added: | 2025-12-19 | |
| Dept Affiliation: |
BIOLOGY
1 Evolution & Ecology Research Centre and the School of Biological, Earth and Environmental Sciences at the University of New South Wales, Sydney, Australia. Electronic address: kyle.morrison@unsw.edu.au. 2 Evolution & Ecology Research Centre and the School of Biological, Earth and Environmental Sciences at the University of New South Wales, Sydney, Australia; Division of Ecology and Evolution, Research School of Biology, The Australian National University, Canberra, Australian Capital Territory, Australia. Electronic address: p.pottier@unsw.edu.au. 3 Evolution & Ecology Research Centre and the School of Biological, Earth and Environmental Sciences at the University of New South Wales, Sydney, Australia. Electronic address: pietro_pollo@hotmail.com. 4 Evolution & Ecology Research Centre and the School of Biological, Earth and Environmental Sciences at the University of New South Wales, Sydney, Australia. Electronic address: l.ricolfi@unsw.edu.au. 5 Evolution & Ecology Research Centre and the School of Biological, Earth and Environmental Sciences at the University of New South Wales, Sydney, Australia. Electronic address: coralie.williams@unsw.edu.au. 6 Evolution & Ecology Research Centre and the School of Biological, Earth and Environmental Sciences at the University of New South Wales, Sydney, Australia; College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China. Electronic address: yefeng.yang1@unsw.edu.au. 7 HortSys, Univ Montpellier, CIRAD, Montpellier, France. Electronic address: beillouin@cirad.fr. 8 Laboratory of Plankton Ecology, Department of Zoology, Institute of Biology, Federal University of Juiz de Fora, Juiz de Fora 36036-900, Brazil. Electronic address: simone.jcardoso@gmail.com. 9 MARE - Marine and Environmental Sciences Centre, ARNET - Aquatic Research Network, Department of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal. Electronic address: veronica@ci.uc.pt. 10 Department of Biology, Concordia University, Montreal, Quebec, Canada. Electronic address: brian.kenneth.gallagher@gmail.com. 11 School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; Institute of Biology, College of Science, University of the Philippines, Diliman, Quezon City, Philippines. Electronic address: jelainegan21@gmail.com. 12 Department of Epidemiology and Statistics, School of Public Health, Guangdong Pharmaceutical University, China. Electronic address: haoguang2015@hotmail.com. 13 Social Determinants of Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran. Electronic address: mr.mojtabakeikha@gmail.com. 14 Department of Ecology and Marine Resources, Institute of Biosciences, Federal University of the State of Rio de Janeiro, Rio de Janeiro, RJ 22290-240, Brazi. Electronic address: betinaksuzuki@unirio.br. 15 ICAR-National Institute of Agricultural Economics and Policy Research (NIAP), New Delhi 110012, India. Electronic address: kiran.tm@icar.gov.in. 16 Institute of Dendrology, Polish Academy of Sciences, Parkowa 5, Kórnik 62-035, Poland. Electronic address: latterini@man.poznan.pl. 17 Department of Ecology, University of Granada 18071 Granada, Spain; Laboratory of Ecology (iEcolab), Inter-University Institute for Earth System Research in Andalusia (IISTA), Granada, Spain. Electronic address: leverkus@ugr.es. 18 Evolution & Ecology Research Centre and the School of Biological, Earth and Environmental Sciences at the University of New South Wales, Sydney, Australia; Charles Perkins Centre, School of Life and Environmental Sciences, The University of Sydney, Sydney, Australia. Electronic address: erin.macartney@sydney.edu.au. 19 Universidad de Alcalá, Departamento de Geología, Geografía y Medio Ambiente, A-II km 33.0, 28 |
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Description: |
Meta-analysis is commonly a core component of systematic reviews and has become an important method to reconcile conflicting findings, increase statistical power, and chart new research directions. However, poor reporting practices make it challenging to evaluate the validity of meta-analyses. Despite the existence of reporting checklists, a specifically designed tool has yet to be developed to appraise the completeness with which a meta-analysis has been reported. To bridge this gap, we introduce the Meta-analysis Appraisal Tool for Environmental Sciences (MATES). To develop MATES, we adapted a Delphi process involving experts in meta-analysis methodologies, researchers with experience in guideline/appraisal tool development, and editors of relevant journals. The Delphi process had five steps, including three workshops (11-16 participants), a survey (193 participants), and a validation task (30 participants). This iterative development process resulted in a 14-item appraisal tool that reflects the environmental science and research syntheses community's consensus on essential elements to appraise the completeness with which a meta-analysis has been reported. Validation across 50 meta-analyses demonstrated that the tool is repeatable (average agreement rate: 88.97 %) and time-efficient to implement (17.00 ± 12.23 min). We also outline guidance for interpreting MATES results, describe its potential applications, and reflect on the development process. The authors provide practical implementation guidance for each MATES item, illustrated with real examples in the supplementary material. We also report an extended development methodology to support reproducibility. Finally, we built created a ShinyApp that includes both a training module and an application tool to enhance the usability of MATES (https://kylemorrisonisshiny99.shinyapps.io/MATES_shiny/). Overall, MATES provides authors, readers, stakeholders, and editors with a reliable and accessible tool for appraising the completeness with which a meta-analysis in environmental sciences has been reported. |
