\section{Conclusion}
In this work, we showed that radiology report generation models continue to struggle with two fundamental issues: the variability of free-text supervision and the strong imbalance between normal and abnormal findings. These limitations reduce the consistency of supervision and bias models toward underreporting clinically important pathologies. We addressed these issues through ALO, a simple data-centric strategy that restructures reports into anatomy-level findings sections and balances the data distribution within each region. Combined with modular anatomy expert models, this approach provides stable supervision and effectively mitigates the dominance of normal findings. Experiments across three public datasets show that ALO substantially improves sensitivity and F1-Score. Our results highlight that a data-centric view of report generation can meaningfully improve clinical reliability. Structuring reports at the anatomy level not only enables targeted oversampling but also allows anatomy-level evaluation, offering more precise insights into model strengths and weaknesses. Ultimately, our work demonstrates that for report generation, improving the supervision signal can be as impactful as architectural innovations.