diff --git a/agents/leo/musings/research-2026-03-22.md b/agents/leo/musings/research-2026-03-22.md
index 9ba790bd9..a3be108da 100644
--- a/agents/leo/musings/research-2026-03-22.md
+++ b/agents/leo/musings/research-2026-03-22.md
@@ -56,7 +56,7 @@ The automation-bias RCT (medRxiv August 2025, NCT06963957) adds a third mechanis
 
 **What this adds to the KB:** The first two mechanisms could be addressed by better training or design. Override errors might decrease with training that specifically targets the tendency to override correct AI outputs. De-skilling might decrease with training that preserves independent practice. But the automation-bias RCT tests EXACTLY this — it is the training response — and finds it insufficient.
 
-CLAIM CANDIDATE for enrichment of [[human-in-the-loop clinical AI degrades to worse-than-AI-alone]]:
+CLAIM CANDIDATE for enrichment of human-in-the-loop clinical AI degrades to worse-than-AI-alone:
 "A randomized clinical trial (NCT06963957, August 2025) demonstrates that 20 hours of AI-literacy training — substantially exceeding typical physician AI education programs and specifically designed to produce critical AI evaluation — is insufficient to prevent automation bias: AI-trained physicians who received deliberately erroneous LLM recommendations showed significantly degraded diagnostic accuracy compared to a control group receiving correct recommendations"
 
 This is an enrichment, not a standalone claim. It extends the existing HITL degradation claim by showing training-resistance is the specific failure mode — the "better training will fix it" response is empirically unavailable.