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21 Data were categorised using NVivo V.8.0.
All data were categorised using Microsoft Office Excel 2007 (Microsoft Corporation, Redmond, Washington, USA).
By critically reviewing the codes and associated memos, the data were categorised using a conceptual mapping process.
These data were categorised, using established cut-off scores, and are shown in Figure 1 for the three assessment scales.
The data were categorised using themes identified in the raw data (predefined categories or 'taxonomy' were not used) [ 23] using a thematic analysis approach.
Some of the virulence-related transcripts which could not be categorised with the InterProScan data were categorised using the information from the Gene Ontology, UniProt, NCBI and referred publications.
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Where we were provided with raw data, children were categorised using the zbmicat function (a Stata add-on program) as normal weight, overweight or obese using age and gender specific IOTF definitions [ 17].
Incidents, where prompting was judged to be needed were categorised using a data-driven analysis as problems in: Sequencing (intrusion, omission and repetition), Finding things (locating and identifying), Operation of appliances, and Incoherence (toying and inactivity).
Pathologies were categorised using the diagram shown in Fig. 1 [6].
BMI values were categorised using WHO criteria.
Responses were categorised using a framework analysis.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com