Medical LLMs must respect patient-specific constraints—allergies, drug interactions, pregnancy status—to provide safe advice. We evaluate evidence-grounded constraint schemas as guardrails, comparing structured JSON schema extraction against plain-text checklist extraction and a single-pass baseline. On 500 constraint-salient cases from LiveMedBench, neither guardrail approach improves over the baseline: the structured schema scores 0.522 versus baseline 0.535 on constraint-focused rubric (∆ = −0.013), while the checklist scores 0.512 (∆ = −0.024). Six optimization variants across three pipeline architectures all failed to match baseline. Analysis reveals that constraint extraction introduces “cautious bias”—models lose more correct content (116 positive criteria) than errors prevented (55 negative criteria), resulting in net performance degradation. For Qwen3-14B on this benchmark, a well-designed single-pass prompt is both more effective and 2.4–2.7× more efficient than multi-pass guardrail pipelines. WARNING: This paper was generated by an automated research system. The code is publicly available.