“highly significant”

Additionally, 46.57% of patients in the PV group were HBeAg-positive, compared to 21.30% in the CVR group, a highly significant difference (P < 0.001).

Highly significant correlations (P< 0.001) were observed between the measured and predicted values for soil pH and the four physicochemical parameters (SOM, AN, AP, and AK), demonstrating the exceptional performance of the established models.

The spatial smoothing term was highly significant ( edf = 5.77, χ 2 = 331.4, p < 0.001), suggesting a strong spatial structure in outbreak patterns.

The one-way ANOVA shown in Table 4 revealed a highly significant difference in HXI scores between these groups (F (3, 98) = 126.18, p < 0.001).

A Welch two-sample t -test confirmed this difference was highly significant ( t = 20.91, p < 0.001, 95% CI [0.20, 0.24]).

The analysis revealed a profound, highly significant proportional asymmetry between thickener types (McNemar's χ 2 = 50.57 ‬, p < 0.001), paired with a negative statistical agreement metric (κ = −0.1085).

The distribution of microbial isolates in corneal cultures showed a highly significant difference between the two study periods (χ 2 = 78.14, p < 0.001).

As shown in Table 3 , the increases in F1 scores for all three models reached a highly significant level ( p < 0.001).

The Mann–Whitney U test indicated that this difference was highly significant ( U = 18, Z = −12.6, p < 0.001), with a large effect size (rank‐biserial, r = 0.86).

The similar efficacy trend of edema inhibition observed in both AiME and the standard treatment is supported by a highly significant statistical difference, with p < 0.001 (***) between the two groups. 3.3.

The statistics revealed that the interaction between insertion depth and bone type was highly significant ( p < 0.001) for both torque and ISQ.

Response times The main effect of cue type was highly significant, with F (2, 68) = 116.79, p < 0.001, η p 2 = 0.77.

The difference was highly significant in every case for HRs ( p < 0.001) and in two of four cases for FARs.

The consistent separation of curves across both time periods and all drug types, combined with highly significant log-rank test results (p<0.001 for all comparisons except famciclovir in period 2), provide strong evidence for a protective association between AHD use and AD treatment initiation ( online supplemental figures S1 and S2 ).

Additionally, family history also showed a highly significant difference ( p = 0.001); (56.7%) of cases in the ASD group and (33.3%) of cases in ID group had positive family history of neurodevelopmental disorders in comparison to control group.

The factorability of the data was confirmed by a high Kaiser-Meyer-Olkin measure (KMO = 0.84) and a highly significant Bartlett’s test of sphericity (χ2 = 11,447, p < .001), indicating sufficient intercorrelations among the variables.

All tumors <30 mm achieved complete resection, while piecemeal retrieval was required in 9.6% of 30–50 mm tumors and 89.5% of ≥50 mm tumors. Univariable Analysis In univariable analysis for piecemeal retrieval, both tumor dimensions were highly significant: longitudinal length (OR = 1.35 per 10 mm, 95% CI: 1.16–1.57, p < 0.001) and transverse length (OR = 1.53 per 10 mm, 95% CI: 1.29–1.81, p < 0.001).

Mixed-effects regression The Time × Group interaction was highly significant (B = 2.46, p < 0.001), confirming greater practice gains in the intervention group (+4.24, p < 0.001) vs. controls (+1.78, p < 0.05).

The body ecotype showed highly significant differences (p < 0.001), with a predominance of the longilinear biotype in males (86.1%).

Log-rank (Mantel–Cox) testing indicated highly significant differences between groups ( χ 2 = 83.260, P < 0.001; Supplementary Table 2 ).

Data analysis revealed highly significant seasonal variations (P< 0.001).

VEGF A & TGF β1 measured in arbitrary units (AU): Both growth factors showed highly significant scores in the CTG immersed in i-PRF analyzed in the CTG + i-PRF group, compared to the CTG alone from the CTG group ( P -value < 0.001), with large effect sizes for both growth factors suggesting a substantial magnitude of difference in growth factors expression between groups ( Table 5 ).

There was a highly significant main effect of Time ( p < 0.001), confirming that hemi‐mMASI decreased significantly during follow‐up in both treatment modalities.

However, on the larger external Kaggle dataset ( n = 693), the differences between the ResNet + DenseNet ensemble and the individual models were highly significant: compared to ResNet50V2 ( Z = 4.816, p < 0.001) and InceptionV3 ( Z = 8.042, p < 0.001).

As shown in Table 5 , the results of testing the hypotheses regarding the correlations among factors influencing learning resilience are as follows: academic self-efficacy and perceived campus belonging exhibit highly significant correlations with learning resilience at the P < 0.001 level; learning climate shows less significant correlation with learning resilience at the P < 0.05 level; Social relationships and learning resilience exhibit a significant correlation at the P < 0.01 level; however, the relationship between learning environment and learning resilience is less significant at the P < 0.05 level.