The crisis of manufactured scholarship: confronting AI-driven “letter-bombing” and profile inflation in medical journals

Post-publication peer review faces a emerging systemic vulnerability from 'letter-bombing'-the proliferation of high-volume, potentially machine-generated critiques that exploit citable correspondence units, creating a plausible risk of academic metric inflation. Driven by the democratization of Large Language Models (LLMs), these automated submissions frequently exploit formatting loopholes to manipulate public indicators like the h-index. The problem is particularly observable in data-intensive specialties like surgical oncology, where abstract-level statistical critiques targeting complex clinical trials risk muddying translational research communication. Drawing on documented bibliometric anomalies and confirmed editorial actions, this piece discusses operational definitions of abnormal correspondence volumes and proposes a multi-layered framework. To protect the scientific record, we propose an enforceable framework for editorial boards: implementing strict metadata labeling to close indexing loopholes, verifying author history, and mandating the "Right of Simultaneous Reply" as a universal baseline publishing standard.