“highly significant”

Our data clarify that onychomycoses prevalence is highly significant in diabetic patients ( p < 0.0001) compared with healthy ones.

ANOVA results indicated that all proposed models were highly significant ( p < 0.0001), whereas lack-of-fit tests were not significant ( p > 0.05), suggesting a strong alignment between the experimental and predicted values.

In particular, the variable methanol % exhibited a highly significant ( p < 0.0001) influence on TPC yield.

28 , 29 , 31 ICIs-related VTE occurrence was not independently associated with shorter PFS (HR 1.82, 95% CI 0.93-3.55, P = .08) ( Figure 3A ), and highly significant heterogeneity was shown between studies (Cochran's Q statistic, P < .0001, I 2 = 89%).

Overall, primary efficacy analysis showed the difference in terminal hair density change at 26 weeks from baseline between lasercomb and sham treatment was highly significant ( p < 0.0001) in both female trials (Fig. 2 a, b).

The proposed models for all responses were highly significant with values ranging from p < 0.0001 to p = 0.0005 (Table 2 ).

The difference between both groups was highly significant ( p < 0.0001).

For all six species, it is lower in overall genomic DNA relative to protein coding DNA (this was highly significant for all six pairwise comparisons, p < 10 −4 , MWU‐test).

There was a highly significant difference in galectin-3 levels among cases and controls when classified into sub-groups based on their LVEF (p<0.0001).

(iii) The mean serum level of HE4 in the OvEndo group was 7.6 ng/mL versus 7.8 ng/mL in the CTR group, and we found it highly significant (p=0.0001).

Whereas the original data analysis reported a highly significant effect ( P < 0.0001), generalized mixed-effects models with a random effect for name indicates that the effect is not robust, neither with the data from the general public (b = −0.108 [log odd units], z = −1.712, P = 0.087) nor with the data from elected officials (b = −0.065, z = −1.432, P = 0.155). * While this does not mean that we should accept the null hypothesis, it shows that the study is underpowered.

One-way ANOVA showed no difference in the mean of ADC values between the groups with a p -value of 0.525, but additional Jonckheere-Terpstra tests showed highly significant ( p < 0.0001) trends towards increasing SWE and decreasing ADC across the groups.

Serum AMH levels had a highly significant reduction with the increasing age (p < 0.0001).

The differences among the groups were highly significant (log-rank P < .0001).

For all heat strain variables, the splines te(T a , p a ) modeling the influence of temperature and humidity were statistically highly significant ( p < 0.0001).

The results of the regression and variance analysis indicated that the regression model was highly significant ( p < 0.0001), while the lack of fit was not significant ( p > 0.05), suggesting a good model fit.

The statistical analysis showed that the determined values of the contact angle and roughness for all the samples are highly significant ( p < 0.0001).

All fitted models were highly significant ( p < 0.0001), with R 2 ranging from 0.72 to 0.99.

For variables showing significant correlations, a significance threshold of p < 0.05 was used, and for highly significant correlations, the threshold of p < 0.0001 was indicated in the results.

However, rPSG1-Fc treatment resulted in highly significant improvements in the ipsilateral/contralateral foot fault ratio at both days 3 ( p <0.0001) and 5 (p = 0.0015) post-ischaemia.

Isolate × Genotype interactions were highly significant in leaves (χ 2 = 83.69), stems (χ 2 = 51.71), and roots (χ 2 = 126.6; all p < 0.0001).

The obtained model was found to be highly significant, as confirmed by the ANOVA analysis ( p < .0001). 3.1.3.

The SII score also indicated a highly significant and strong predictive association with SIRS development, with a hazard ratio of 3.47 ( p < 0.0001).

Interactions between late referral and time on dialysis, and dialysis modality and time on dialysis were tested and found to be highly significant ( P < 0.0001).

The multiple linear regression analysis showed that second-order polynomial models were adequately accurate to predict the relevant responses because not only were highly significant ( p < 0.0001), but also had insignificant lack-of-fit values ( Table 4 and Table 5 ).