Letter to the Editor: Reconsidering the measurement and interpretation of unwanted e-mail burden in academic surgery

HIGHLIGHTS Critically examines the construct validity of message counts as a surrogate for digital burden in surgery. Identifies unmeasured structural determinants that may confound associations between unwanted e-mail (UEM) burden and academic metrics. Highlights the heterogeneous risk profiles of UEM categories with implications for institutional policy. Emphasizes the need for longitudinal assessment to capture temporal dynamics in academic solicitation cycles. Proposes a multidimensional framework to guide technical, organizational, and policy interventions for UEM mitigation. To the Editor, We read with great interest the study by Bernardi et al[1], which offers a timely assessment of UEMs within an academic surgical department and highlights an increasingly recognized dimension of digital workload in surgical practice. As e-mail traffic grows alongside open-access publishing, conference marketing, and automated academic outreach, understanding its effect on clinicians is becoming a relevant issue for workforce well-being and institutional communication systems. While the authors provide valuable initial evidence, several conceptual and methodological aspects merit further consideration. This study complies with the TITAN 2025 framework for transparent and ethical reporting of AI-assisted research[2]. First, relying solely on message counts as the primary endpoint assumes that volume alone represents digital burden, yet the construct validity of this metric is unclear. Counts do not reflect the content, tone, or timing of messages and thus cannot distinguish differences in cognitive load, workflow disruption, or downstream effects on time-critical communication, research productivity, or emotional strain[3]. Because surgical work is highly sensitive to interruptions and information noise, future studies should link UEM exposure to objective behavioral indicators and validated measures of occupational well-being to determine whether these messages constitute a clinically relevant and modifiable risk. Second, the associations observed between UEM burden and academic metrics such as seniority, publication count, and H-index may be influenced by unmeasured structural determinants of exposure. Editorial responsibilities, professional society roles, publicly indexed profiles on scholarly platforms, and administrative duties all alter an individual’s visibility within academic communication networks and therefore their likelihood of receiving unsolicited messages[4]. Without accounting for these digital-footprint and organizational-role variables, correlations may conflate structural targeting with intrinsic scholarly productivity. A multilevel analytical framework separating personal academic activity from network-driven exposure would clarify causal mechanisms and inform targeted institutional strategies to reduce disproportionate digital noise among highly visible clinicians[5]. Third, treating UEM as a single homogeneous category obscures important heterogeneity in risk and policy relevance. Benign newsletters, low-stakes advertisements, predatory journal solicitations, high-cost conference invitations, and messages resembling fraudulent schemes carry different implications for cybersecurity, academic integrity, and financial safety. Aggregation into a single outcome limits the study’s applicability to institutional governance. A structured, risk-stratified classification system that distinguishes credibility, financial risk, and potential scientific harm would allow more precise filtering policies, faculty training, and research-integrity safeguards. Finally, a one-month cross-sectional sample cannot capture the temporal dynamics of academic solicitation. Calls for manuscripts and conference announcements often follow predictable cycles aligned with major congress deadlines and publisher marketing schedules. Without accounting for seasonal variability, estimated UEM prevalence may overstate or understate typical exposure. Longitudinal monitoring across multiple academic cycles would better describe temporal patterns and support anticipatory filtering or algorithmic mitigation strategies[6]. Taken together, these considerations suggest that UEM should be understood as a multifaceted exposure shaped by behavioral, organizational, and technological factors. More granular measurement, attention to structural determinants of digital visibility, and risk-stratified classification could support smarter technical solutions, including adaptive filters and AI-assisted triage that prioritize clinically relevant communication. At the policy level, institutions should consider guidance on professional contact information, transparent workload for editorial and committee roles, and education on predatory outreach to ensure that surgeons are not disproportionately burdened by digital noise as their academic visibility grows. Embedding UEM within this broader agenda may encourage more deliberate, ergonomically robust communication infrastructures across surgical systems.