Beyond Aggregate Scores: Revealing Conditional Model Behavior in Chest X-Ray Nodule Detection

Aggregate model performance can rank systems without revealing the conditions under which their behavior diverges. We introduce PULSE, a controlled-response evaluation framework, and instantiate it in chest X-ray nodule detection. Analytic nodule-like probes vary in size, contrast, morphology and position on fixed chest radiographs, making fourteen detectors (seven families, two seeds each) directly comparable under matched conditions. The resulting behavioral atlas reveals sharply different behaviors behind similar conventional performance. At the largest tested probe size, increasing requested contrast drives Faster R-CNN toward saturation but reduces detection in both DINO seeds. Changing the intensity profile from a smooth disk to a peak-matched Gaussian also produces markedly different losses across models. These are structured differences: response-profile distances recur across discovery and held-out host banks (Spearman $\rho =0.999$), and every model is closest to its other seed. An earlier prespecified six-model study confirms the coarse geometry and regional patterns. Measurement-design analyses show that recovering an ordering, finding disagreements and reconstructing a response field require different sampling strategies. A matched natural-lesion assay provides partial pointwise behavioral support. Controlled evaluation makes conditional model behavior visible, reproducible and testable, complementing aggregate performance.