Keyword search (4,163 papers available)

"Accuracy" Keyword-tagged Publications:

Title Authors PubMed ID
1 Are MEDLINE searches sufficient for systematic reviews and meta-analyses of the diagnostic accuracy of depression screening tools? A review of meta-analyses Rice DB; Kloda LA; Levis B; Qi B; Kingsland E; Thombs BD; 27411746
LIBRARY
2 Reporting quality in abstracts of meta-analyses of depression screening tool accuracy: a review of systematic reviews and meta-analyses Rice DB; Kloda LA; Shrier I; Thombs BD; 27864250
LIBRARY
3 Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data Thölke P; Mantilla-Ramos YJ; Abdelhedi H; Maschke C; Dehgan A; Harel Y; Kemtur A; Mekki Berrada L; Sahraoui M; Young T; Bellemare Pépin A; El Khantour C; Landry M; Pascarella A; Hadid V; Combrisson E; O' Byrne J; Jerbi K; 37385392
IMAGING
4 How uncertainty affects information search among consumers: a curvilinear perspective He S; Rucker DD; 36471868
JMSB
5 Transparency and completeness of reporting of depression screening tool accuracy studies: A meta-research review of adherence to the Standards for Reporting of Diagnostic Accuracy Studies statement Nassar EL; Levis B; Neyer MA; Rice DB; Booij L; Benedetti A; Thombs BD; 36047034
PSYCHOLOGY
6 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
7 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
8 A Simulation Toolkit for Testing the Sensitivity and Accuracy of Corticometry Pipelines OmidYeganeh M; Khalili-Mahani N; Bermudez P; Ross A; Lepage C; Vincent RD; Jeon S; Lewis LB; Das S; Zijdenbos AP; Rioux P; Adalat R; Van Eede MC; Evans AC; 34381348
PERFORM
9 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
10 Equivalency of the diagnostic accuracy of the PHQ-8 and PHQ-9: a systematic review and individual participant data meta-analysis Wu Y; Levis B; Riehm KE; Saadat N; Levis AW; Azar M; Rice DB; Boruff J; Cuijpers P; Gilbody S; Ioannidis JPA; Kloda LA; McMillan D; Patten SB; Shrier I; Ziegelstein RC; Akena DH; Arroll B; Ayalon L; Baradaran HR; Baron M; Bombardier CH; Butterworth P; Carter G; Chagas MH; Chan JCN; Cholera R; Conwell Y; de Man-van Ginkel JM; Fann JR; Fischer FH; Fung D; Gelaye B; Goodyear-Smith F; Greeno CG; Hall BJ; Harrison PA; Härter M; Hegerl U; Hides L; Hobfoll SE; Hudson M; Hyphantis T; Inagaki M; Jetté N; Khamseh ME; Kiely KM; Kwan Y; Lamers F; Liu SI; Lotrakul M; Loureiro SR; Löwe B; McGuire A; Mohd-Sidik S; Munhoz TN; Muramatsu K; Osório FL; Patel V; Pence BW; Persoons P; Picardi A; Reuter K; Rooney AG; Santos IS; Shaaban J; Sidebottom A; Simning A; Stafford L; Sung S; Tan PLL; Turner A; van Weert HC; White J; Whooley MA; Winkley K; Yamada M; Benedetti A; Thombs BD; 31298180
LIBRARY
11 Diagnostic accuracy of the Depression subscale of the Hospital Anxiety and Depression Scale (HADS-D) for detecting major depression: protocol for a systematic review and individual patient data meta-analyses. Thombs BD, Benedetti A, Kloda LA, Levis B, Azar M, Riehm KE, Saadat N, Cuijpers P, Gilbody S, Ioannidis JP, McMillan D, Patten SB, Shrier I, Steele RJ, Ziegelstein RC, Loiselle CG, Henry M, Ismail Z, Mitchell N, Tonelli M 27075844
LIBRARY
12 Gesture-based registration correction using a mobile augmented reality image-guided neurosurgery system. Léger É, Reyes J, Drouin S, Collins DL, Popa T, Kersten-Oertel M 30800320
PERFORM

 

Title:Diagnostic accuracy of the Depression subscale of the Hospital Anxiety and Depression Scale (HADS-D) for detecting major depression: protocol for a systematic review and individual patient data meta-analyses.
Authors:Thombs BDBenedetti AKloda LALevis BAzar MRiehm KESaadat NCuijpers PGilbody SIoannidis JPMcMillan DPatten SBShrier ISteele RJZiegelstein RCLoiselle CGHenry MIsmail ZMitchell NTonelli M
Link:https://www.ncbi.nlm.nih.gov/pubmed/27075844?dopt=Abstract
DOI:10.1136/bmjopen-2016-011913
Publication:BMJ open
Keywords:Chronic illnessDiagnostic accuracyIndividual Patient Data Meta-AnalysisMajor depressionPRIMARY CAREScreening
PMID:27075844 Category:BMJ Open Date Added:2019-06-07
Dept Affiliation: LIBRARY
1 Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada Department of Psychiatry, McGill University, Montreal, Québec, Canada Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada Department of Medicine, McGill University, Montreal, Québec, Canada Department of Educational and Counselling Psychology, McGill University, Montreal, Québec, Canada Department of Psychology, McGill University, Montreal, Québec, Canada.
2 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada Department of Medicine, McGill University, Montreal, Québec, Canada Respiratory Epidemiology and Clinical Research Unit, McGill University Health Centre, Montreal, Québec, Canada.
3 Department of Libraries, Concordia University, Montreal, Québec, Canada.
4 Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada.
5 Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.
6 Department of Clinical, Neuro and Developmental Psychology and EMGO Institute, VU University Amsterdam, Amsterdam, The Netherlands.
7 Department of Health Sciences, Hull York Medical School, University of York, York, UK.
8 Department of Medicine, Health Research and Policy, Stanford Prevention Research Center, Stanford School of Medicine, Stanford, California, USA Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, California, USA.
9 Department of Community Health Sciences, University of Calgary, Calgary, Edmonton, Canada Department of Psychiatry, University of Calgary, Calgary, Edmonton, Canada.
10 Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada Department of Mathematics and Statistics, McGill University, Montreal, Québec, Canada.
11 Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
12 Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada Department of Oncology, McGill University, Montreal, Québec, Canada.
13 Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada Department of Clinical Neurosciences, University of Calgary, Calgary, Edmonton, Canada.
14 Department of Community Health Sciences, University of Calgary, Calgary, Edmonton, Canada Department of Oncology, McGill University, Montreal, Québec, Canada.
15 Department of Psychiatry, University of Alberta, Edmonton, Alberta, Canada.
16 Department of Medicine, University of Calgary, Calgary, Edmonton, Canada.

Description:

Diagnostic accuracy of the Depression subscale of the Hospital Anxiety and Depression Scale (HADS-D) for detecting major depression: protocol for a systematic review and individual patient data meta-analyses.

BMJ Open. 2016 Apr 13;6(4):e011913

Authors: Thombs BD, Benedetti A, Kloda LA, Levis B, Azar M, Riehm KE, Saadat N, Cuijpers P, Gilbody S, Ioannidis JP, McMillan D, Patten SB, Shrier I, Steele RJ, Ziegelstein RC, Loiselle CG, Henry M, Ismail Z, Mitchell N, Tonelli M

Abstract

INTRODUCTION: The Depression subscale of the Hospital Anxiety and Depression Scale (HADS-D) has been recommended for depression screening in medically ill patients. Many existing HADS-D studies have used exploratory methods to select optimal cut-offs. Often, these studies report results from a small range of cut-off thresholds; cut-offs with more favourable accuracy results are more likely to be reported than others with worse accuracy estimates. When published data are combined in meta-analyses, selective reporting may generate biased summary estimates. Individual patient data (IPD) meta-analyses can address this problem by estimating accuracy with data from all studies for all relevant cut-off scores. In addition, a predictive algorithm can be generated to estimate the probability that a patient has depression based on a HADS-D score and clinical characteristics rather than dichotomous screening classification alone. The primary objectives of our IPD meta-analyses are to determine the diagnostic accuracy of the HADS-D to detect major depression among adults across all potentially relevant cut-off scores and to generate a predictive algorithm for individual patients. We are already aware of over 100 eligible studies, and more may be identified with our comprehensive search.

METHODS AND ANALYSIS: Data sources will include MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, PsycINFO and Web of Science. Eligible studies will have datasets where patients are assessed for major depression based on a validated structured or semistructured clinical interview and complete the HADS-D within 2 weeks (before or after). Risk of bias will be assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Bivariate random-effects meta-analysis will be conducted for the full range of plausible cut-off values, and a predictive algorithm for individual patients will be generated.

ETHICS AND DISSEMINATION: The findings of this study will be of interest to stakeholders involved in research, clinical practice and policy.

PMID: 27075844 [PubMed - indexed for MEDLINE]





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