In three-dimensional medical image classification, higher input resolution simultaneously changes both the native anatomical detail available to a model and the spatial grid on which network downsampling stages operate. We disentangle these factors on OrganMNIST3D, an 11-class organ classification task derived from abdominal CT, evaluating native 28³ (N28) and 64³ (N64) inputs across four 3D architectures—ResNet-18, ResNet-50, DenseNet-121, and Swin-Tiny—with five seeds per condition and 10,000-replicate paired stratified hierarchical bootstrap 95% confidence intervals. N64 yields statistically resolved gains across all three primary metrics—accuracy, Macro-AUC, and Macro-F1—for ResNet-18 and Swin-Tiny, in accuracy alone for DenseNet-121, and in no metric for ResNet-50. To test whether these gains require native high-resolution detail, ResNet-18 and Swin-Tiny additionally receive same-information enlargement controls (trilinear and nearest-neighbor upsampling of N28 to 64³) and an information-ablation bottleneck (N64 pooled to 28³ and then restored to 64³). Each control improves over N28 with resolved intervals, while no control-versus-N64 contrast is statistically resolved, indicating that operational grid scale, rather than finer native content, contributes substantially to the observed gains. However, all 64³-grid conditions incur 3.4×–7.4× greater training time and 2.7×–8.8× greater peak GPU memory. Resolution selection in volumetric classification should therefore account for grid-scale confounds alongside architecture-specific uncertainty and computational cost.