An Empirical Study of Development-Tuned Representation Sensitivity for Medical QA Data Selection

We study medical question-answering (QA) data selection when a developer already has questions, labels, and auxiliary explanatory texts but can retain only B examples for a final LoRA update. Representation-Sensitivity Prioritization first restricts the candidate bank to its top-4B predictive-entropy examples, then combines the frequency of answer flips with non-flipping contraction of the top-two answer margin under hidden-state perturbations. We evaluate this operational score on five medical QA datasets with google/medgemma-4b-it, (B=128), and three seed branches. After removing four MedExpQA test questions that also occur in its acquisition pool, dataset-specific development-tuned \method{} attains the highest observed five-dataset mean accuracy (53.44%) and Macro-F1 (48.21%) among the evaluated selectors. Relative to random selection within the same entropy-restricted pool, the descriptive differences are +1.49 and +2.10 percentage points, respectively; paired 95% bootstrap intervals include zero. The audit mixes benchmarks with different relationships to documented backbone post-training sources, and the same test cohorts had been used in earlier analyses before the expanded tuning grid was designed. Results are therefore exploratory, modest, and dataset-dependent rather than evidence of statistical superiority. Because every selected training example includes auxiliary text and no matched answer-only control is evaluated, the experiments also do not identify a causal benefit from auxiliary-text conditioning itself.