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     Long Response


    Visualizing AUSDRISK changes in Shae platform users — by sex and follow-up (age stratification not provided)

    Figures first — data come from a retrospective analysis of Shae platform users (May 2014–Mar 2018; N=1,690). Provided numeric results include AUSDRISK at baseline and follow-up by sex and several anthropometrics; age-group stratified AUSDRISK values were not provided in the shared dataset, so graphs below show the available, directly reported metrics. All claims and numbers are inline-cited below.

    Key numeric takeaways (directly from the study):
    • Females: AUSDRISK decreased from 8.95 to 7.87 (reported by authors) — a reduction of 1.08 score units.
    • Males: AUSDRISK decreased from 10.82 to 9.79 — a reduction of 1.03 score units.
    • Weighted overall AUSDRISK (computed from reported Ns & means) fell ≈ 9.20 → 8.12 (≈ −1.08 units, ≈ −11.7%).

    Important: the shared paper did not supply AUSDRISK values stratified by age-bracket in the extracted dataset; therefore age-specific plots cannot be produced without the underlying raw data. The authors did adjust models for age and follow-up duration, and they reported stronger anthropometric changes in women — see citation below for methods, limitations, and full results.

    Primary source (study details & methods)

    Next steps & limitations

    • Age-stratified AUSDRISK plots are not possible from the extracted dataset because the paper excerpt did not report AUSDRISK by age bands or supply the raw individual-level data.
    • Follow-up-duration stratification also cannot be plotted because numeric follow-up-time distributions were not supplied in the extract.
    • To produce age-by-follow-up visualizations we need either: (a) the study's individual-level dataset, or (b) the paper's supplemental tables with AUSDRISK by age groups and follow-up times.


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    Updated: March 17, 2026

     Analysis Wizard



    Preparing code to ingest individual-level Shae data (AUSDRISK, age, sex, follow-up duration), compute age-grouped means and GAM regressions, and output Plotly-ready JSON for interactive visualizations.



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