Research Data Leeds Repository

Citation

Comer, Christine and Ammendolia, Carlo and Battié, Michele C. and Bussières, André and Fairbank, Jeremy and Haig, Andrew and Melloh, Markus and Redmond, Anthony and Schneider, Michael J. and Standaert, Christopher J. and Tomkins-Lane, Christy and Williamson, Esther and Wong, Arnold Y. L. (2022) Lumbar Spinal Stenosis treatment algorithm consensus - an international Delphi study. University of Leeds. [Dataset] https://doi.org/10.5518/1106

Dataset description

Datasets from three survey rounds associated with study to develop a consensus treatment algorithm for people with Lumbar Spinal Stenosis.

Additional information:	This dataset is held in the Restricted Access Data Repository, RADAR. To request access, please click the link under ‘Related resources’. You will be prompted to complete a request form including your intended use of the dataset. You will then receive an initial response to your request within 10 working days.
Keywords:	Lumbar spinal stenosis; consensus; Delphi technique; survey; experts; algorithm; clinical practice guideline
Subjects:	A000 - Medicine & dentistry
Divisions:	Faculty of Medicine and Health > School of Medicine
Related resources:	Location Type https://radar.researchdata.leeds.ac.uk/27/ Dataset https://doi.org/10.1186/s12891-022-05485-5 Publication https://eprints.whiterose.ac.uk/188979/ Publication
Date deposited:	04 Mar 2022 17:30
URI:	https://archive.researchdata.leeds.ac.uk/id/eprint/934
Additional details Additional details Alternative title: LSS treatment consensus Creators or authors: Creators ORCID Other ID Email Primary affiliation Primary affiliation ID Primary affiliation ID type Secondary affiliation Secondary affiliation ID Secondary affiliation ID type Comer, Christine https://orcid.org/0000-0001-9847-9408 University of Leeds https://ror.org/024mrxd33 ror Ammendolia, Carlo https://orcid.org/0000-0003-4417-4961 Mount Sinai Hospital, Toronto https://ror.org/05deks119 ror Battié, Michele C. https://orcid.org/0000-0001-8499-2040 Western University https://ror.org/02grkyz14 ror Bussières, André https://orcid.org/0000-0002-2818-6949 McGill University https://ror.org/01pxwe438 ror Fairbank, Jeremy https://orcid.org/0000-0002-0050-874X University of Oxford https://ror.org/052gg0110 ror Haig, Andrew https://orcid.org/0000-0003-0246-6835 University of Michigan https://ror.org/00jmfr291 ror Melloh, Markus https://orcid.org/0000-0002-8819-799X Victoria University of Wellington https://ror.org/0040r6f76 ror Redmond, Anthony https://orcid.org/0000-0002-8709-9992 University of Leeds https://ror.org/024mrxd33 ror Schneider, Michael J. https://orcid.org/0000-0001-7190-3379 University of Pittsburgh https://ror.org/01an3r305 ror Standaert, Christopher J. https://orcid.org/0000-0001-6808-355X University of Pittsburgh https://ror.org/01an3r305 ror Tomkins-Lane, Christy https://orcid.org/0000-0002-0416-8965 Mount Royal University https://ror.org/04evsam41 ror Williamson, Esther https://orcid.org/0000-0003-0638-0406 University of Exeter https://ror.org/03yghzc09 ror Wong, Arnold Y. L. https://orcid.org/0000-0002-5911-5756 Hong Kong Polytechnic University https://ror.org/0030zas98 ror Type of data: Dataset Research funders: NIHR Grant numbers: ICA-CL-2017-03-015 Publication date: 2022 Resource language: English Metadata language: English Publisher: University of Leeds Contact email: c.comer@leeds.ac.uk Last Modified: 26 Nov 2024 09:20