A Machine Learning-Based Analytic Pipeline Applied to Clinical and Serum IgG Immunoproteome Data To Predict Chlamydia trachomatis Genital Tract Ascension and Incident Infection in Women.

Microbiol Spectr · 2023 · PMC10434056 · PMID 37318345

Europe PMC DOI

hedged sentences

0.0600

closest p · 1.2× alpha

0.0680

boldest claim

The sentences

borderline significantP = 0.06so close (0.05 < p ≤ 0.1)

The direct effect of anti-CT123/AccB on reducing the risk of ascension was significant ( P = 0.049) but was negated slightly by an insignificant indirect effect, leading to a borderline significant total effect ( P = 0.06) ( Table 9 and Fig. 7B ).

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marginally significantP = 0.068so close (0.05 < p ≤ 0.1)

7A ), driven by significant indirect effect mediated through reducing burden ( P = 0.024) and marginally significant direct effect ( P = 0.068).

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