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The workshop also discussed how these data might be coded against the conceptual framework [ 98, 99].
For example, a segment of the data might be coded as "serodiscordant couples," and then also have the code "Barrier – Stigma" applied.
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And these data might be even more heavily coded - "encrypted" - than they already are.
These data might be subject to possible coding errors, where some diagnoses may be missed or used incorrectly.
Of course, ideally further validation of the coding should be conducted; linkage to laboratory data might be one way of achieving this.
Due to the normal reference ranges of peripapillary RNFL thicknesses and probability codes can differ according to race, the normative data might be needed for every ethnicity.
Use of DRG, however, can lead to coding adjustments or manipulations, as hospitals may be more likely to report diagnosis and procedure codes that result in larger reimbursements while some data might be ignored simply because of no financial incentives.
The coding of adverse events by the original investigators raised the possibility that some other data might be unreliable.
However, data will be coded as self-report or proxy to explore potential patterns as they might emerge.
The fat thickness data were also coded as a threshold trait to determine how important such measurement error might be for discrete traits, where individuals may be coded as affected or unaffected.
All data analysis was coded and anonymised.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com