Development and validation of a machine learning-based risk prediction model for sarcopenia in community hospital patients: a retrospective cohort study.

Front Aging · 2026 · PMC12979493 · PMID 41836318

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hedged sentences

0.0010

closest p · 0.0× alpha

0.5910

boldest claim

The sentences

highly significantp < 0.001actually significant

The SARC_Cal_score was a consistent, highly significant positive predictor across all models (CatBoost: OR = 2.932 (2.096–4.305), p < 0.001; LightGBM: OR = 2.795 (1.899–4.51), p < 0.001; GBDT: OR = 4.475 (2.934–7.334), p < 0.001).

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did not reach statistical significanceP = 0.591not close (p > 0.1)

Similarly, the CatBoost-based model showed significantly better reclassification than the GBDT-based model (NRI = 0.9938, 95% CI: 0.8661–1.1214; P < 0.0001), although the corresponding IDI did not reach statistical significance (IDI = 0.0057, 95% CI: −0.0151–0.0265; P = 0.591) ( Table 8 ).

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