Organizing Benzodiazepine Withdrawal-Related Symptoms: A Provisional Ontology and Severity-Allocation Analysis
Year: 2026
Type: preprint
Study design: Observational Study
Abstract: Background. Benzodiazepine withdrawal can involve many symptoms across different body systems. Existing symptom lists and rating scales help identify and measure these symptoms, but they do not show how the full symptom vocabulary fits together. We organized a community-compiled withdrawal-symptom list into a provisional, phenomenology-first ontology and then examined where patient-reported severity concentrated within that framework. Methods. We analyzed cross-sectional intake data from 39 adults presenting to a specialty benzodiazepine taper practice (37 presenting for taper care; 2 symptomatic after completing a taper). In Analysis A, 243 original symptom phrases from a publicly available benzodiazepine withdrawal-symptom list were decomposed into 278 analyzable rows and organized into 171 provisional symptom nodes. Each node was mapped by the author to one or more of 17 regulatory systems using a written codebook. In Analysis B, this frozen map was applied to patients’ 0–10 ratings on a 233-item questionnaire using severity-conserving fractional attribution, with 95% bootstrap confidence intervals. Robustness was examined using alternative analytic choices and a separate blinded AI coding pass. Results. The highest-rated individual symptoms were anxiety (mean 7.3/10), insomnia (6.5), hypersensitivity to stress (5.5), nervousness (5.4), loss of confidence (5.4), and depression (5.2). Under the author’s coding, 157 of 171 symptom nodes (91.8%) mapped to two or more regulatory systems. The largest allocated severity shares mapped to interoceptive regulation (14.3%), state-level regulation (13.5%), cognitive control (10.2%), and threat-arousal regulation (10.0%). When severity was summarized as the mean rating of questionnaire items contributing to each system, cognitive control ranked highest (3.23/10), followed by affective/reward regulation (3.09/10) and threat-arousal regulation (2.79/10). Across robustness checks, interoceptive regulation, cognitive control, and threat-arousal regulation consistently ranked in the top five, and affective/reward regulation ranked in the top six. Two features were coding-dependent: under the blinded AI coding pass, 70 of 171 nodes (40.9%) were multi-system, and state-level regulation fell from second to twelfth, while affective/reward regulation rose to first. Conclusions. This study organized a broad vocabulary of benzodiazepine withdrawal-related symptoms into 171 provisional symptom nodes mapped to 17 regulatory systems, providing a structured approach to understanding complex symptom experiences. Applying this framework to patient-reported severity ratings identified interoceptive regulation, cognitive control, and threat-arousal regulation as consistently prominent across alternative analyses, with affective/reward regulation also prominent. Although some findings varied with coding decisions, the framework provides a transparent, auditable approach to organizing symptoms and examining their severity across overlapping regulatory systems. Further research may help refine the framework and assess its applicability to other clinical populations.
Source: Research Square
Author: Valsa Madhava
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7225347387