Modern code review is central to software quality, but its effectiveness depends on reviewer expertise, change characteristics, and how tools present changes. Most platforms display modified files alphabetically by default, although our prior work shows that developers find this ordering cognitively misaligned with how they understand multi-file pull requests. Whether these ordering-related attention patterns affect outcomes at scale remains unclear. We present a large-scale study of file ordering and review effectiveness, mining 330,343 multi-file pull requests and 756,814 file instances from 182 GitHub projects in five programming languages. We examine whether file position is associated with later bug-fixing changes, whether pull request size moderates this relationship, and whether reviewer attention, proxied by comments, aligns with latent bug outcomes. Results show statistically significant but modest associations among file position, pull request size, review activity, and latent bug likelihood. Latent bug rates rise from 56.7% at position 1 to 61.5% at position 30. Pull request size has a non-linear relationship with latent bug likelihood: pull requests of about ten files have the lowest risk, while very small and very large ones have elevated rates. Hurdle models show that attention is diluted as pull request size grows: each additional modified file reduces the odds of receiving any review comment by about 8.7%. These findings reveal an attention-effectiveness gap: visible review activity does not necessarily prevent defects. Alphabetical ordering is therefore not a neutral interface default, but a structural feature shaping attention allocation, review coverage, and confidence in review outcomes. We propose context-aware file ordering, dependency-aware grouping, risk-aware prioritization, and per-file coverage indicators to make review attention more visible and actionable.

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