Contextual determinants of adverse event reporting: an evidence map and methods proposal to reduce misclassification in E2B(R3) individual case safety report narratives

Contextual determinants of adverse event reporting: an evidence map and methods proposal to reduce misclassification in E2B(R3) individual case safety report narratives | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 February 2026 V1 Latest version Share on Contextual determinants of adverse event reporting: an evidence map and methods proposal to reduce misclassification in E2B(R3) individual case safety report narratives Author : Eik Niederlohmann 0009-0005-6803-3399 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177194355.58214167/v1 187 views 63 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Purpose: Individual case safety reports (ICSRs) are central to post-marketing pharmacovigilance, yet their narrative sections often lack the contextual information needed to differentiate pharmacologic adverse drug reactions from expectancy- and stress-related symptom formation. We mapped how psychophysiological/nocebo mechanisms are addressed in current pharmacovigilance standards and narrative analytics, and developed a minimal, E2B(R3)-compatible enhancement of ICSR narratives. Methods: We conducted an evidence map using scoping-review methods (PRISMA-ScR). On 18 February 2026, we searched PubMed (MEDLINE), Europe PMC, OpenAlex and IEEE Xplore (2005-17 February 2026) using pre-specified queries combining pharmacovigilance/ICSR narrative concepts with NLP/signal detection and psychophysiology/nocebo terms; we additionally screened the first 200 Google Scholar results (sorted by relevance). Records were exported to Zotero, deduplicated, and screened in Rayyan; full texts were retrieved where available and assessed for eligibility. Results: We identified 966 records; 660 unique records were screened, 350 full texts were assessed, and 250 sources were included for charting. Included sources clustered into narrative analytics/NLP (n=125), narrative data quality and standards (n=87), operationalization of context determinants (n=33), and nocebo/psychophysiology mechanisms (n=5). Conclusions: We propose a short micro-template placed in existing narrative fields plus a 3-level CPD flag (unlikely/possible/likely) as a context covariate for pharmacovigilance workflows. The proposal aims to reduce misclassification and spurious signals while remaining compatible with E2B(R3), low-burden for reporters, and safeguarded against stigmatizing interpretations. Contextual determinants of adverse event reporting: an evidence map and methods proposal to reduce misclassification in E2B(R3) individual case safety report narratives Running title: Context-rich ICSRs for drug safety Eik Niederlohmann, MD 1,2 1 Department of Psychosomatic Medicine and Psychotherapy, Kliniken Erlabrunn gGmbH, Breitenbrunn, Germany 2 Private practice, Leipzig, Germany Correspondence to: Eik Niederlohmann, Kliniken Erlabrunn gGmbH, Breitenbrunn, Germany; Email: [email protected] Abstract Purpose: Individual case safety reports (ICSRs) are central to post-marketing pharmacovigilance, yet their narrative sections often lack the contextual information needed to differentiate pharmacologic adverse drug reactions from expectancy- and stress-related symptom formation. We mapped how psychophysiological/nocebo mechanisms are addressed in current pharmacovigilance standards and narrative analytics, and developed a minimal, E2B(R3)-compatible enhancement of ICSR narratives. Methods: We conducted an evidence map using scoping-review methods (PRISMA-ScR). On 18 February 2026, we searched PubMed (MEDLINE), Europe PMC, OpenAlex and IEEE Xplore (2005-17 February 2026) using pre-specified queries combining pharmacovigilance/ICSR narrative concepts with NLP/signal detection and psychophysiology/nocebo terms; we additionally screened the first 200 Google Scholar results (sorted by relevance). Records were exported to Zotero, deduplicated, and screened in Rayyan; full texts were retrieved where available and assessed for eligibility. Results: We identified 966 records; 660 unique records were screened, 350 full texts were assessed, and 250 sources were included for charting. Included sources clustered into narrative analytics/NLP (n=125), narrative data quality and standards (n=87), operationalization of context determinants (n=33), and nocebo/psychophysiology mechanisms (n=5). Conclusions: We propose a short micro-template placed in existing narrative fields plus a 3-level CPD flag (unlikely/possible/likely) as a context covariate for pharmacovigilance workflows. The proposal aims to reduce misclassification and spurious signals while remaining compatible with E2B(R3), low-burden for reporters, and safeguarded against stigmatizing interpretations. Key points A meaningful fraction of reported ‘side effects’ likely reflects expectancy- and stress-related symptom formation, which can be misattributed to the drug and propagate as signal noise. Current ICSR standards rely heavily on free text for clinical reasoning but do not systematically capture psychophysiological context as a feature usable for case review or signal analytics. A minimal COPEDS-informed narrative micro-template and a non-diagnostic CPD flag can make state-dependent vulnerability visible without changing the E2B(R3) data model. Implementations should treat the flag as a contextual covariate (not a causality label), with training, auditing, and governance to prevent misuse. Future work should test feasibility, inter-rater agreement, and impact on duplicate handling and stratified disproportionality analyses. Keywords: pharmacovigilance; individual case safety reports; adverse event reporting; nocebo; psychophysiology; misclassification; narrative; E2B(R3); COPEDS; signal detection Manuscript information: Main text word count ≈ 1910. Figures: 1. Tables: 3. Introduction Individual case safety reports (ICSRs) remain the fundamental input to post-marketing pharmacovigilance and the primary substrate for many signal-management workflows.1-4 Although key structured elements are specified in E2B(R3), the narrative and reporter-comment fields are where time course, context, and clinical reasoning can be expressed with enough fidelity to support case assessment, duplicate handling and downstream analytics.1-4 A clinically important fraction of reported ‘side effects’ is plausibly driven by contextual determinants—negative expectations, heightened interoceptive focus, acute stressors, and autonomic arousal—that generate or amplify bodily sensations which are then attributed to the drug.6-10 Placebo-arm data across therapeutic areas show that adverse events are common even without pharmacologic exposure, and that framing and prior beliefs shape what is experienced and reported.6-10 In spontaneous reports, similar phenomena can be misclassified as adverse drug reactions, inflating false positives, consuming pharmacovigilance resources and, in the worst case, obscuring true safety signals through added noise and heterogeneity.5,13-15 From a clinical standpoint, these reactions are not ‘vague somatization’ but patterned outputs of stress-regulated systems (autonomic, endocrine, and sensorimotor) that can produce dizziness, dyspnea, paresthesia, palpitations, gastrointestinal symptoms, and transient cognitive-perceptual changes.11 Such symptom patterns are particularly likely in settings that are intrinsically anxiety-provoking (e.g., hospitalization, newly diagnosed illness) or that provide salient threat cues (e.g., informed-consent discussions, patient information leaflets, safety communications).6-9 To make these contextual determinants visible in pharmacovigilance without introducing psychiatric labels, we focus on a narrowly defined and operationalizable construct: cognitive-perceptual disruption (CPD). COPEDS is a brief self-report screening instrument developed to operationalize CPD-prone states and may be suitable as a non-diagnostic indicator of state-dependent vulnerability at the time symptoms emerge.12 We treat CPD/COPEDS here as data-quality-relevant context—not as a psychotherapy theory—because it can help adjudicate attribution, reduce misclassification, and improve the feature space available for narrative analytics. The aims of this paper are twofold: (i) to map how psychophysiology and expectancy/nocebo mechanisms are currently represented in pharmacovigilance standards and narrative-analytics research, and (ii) to propose a minimal, E2B(R3)-compatible micro-template and a 3-level CPD flag that encodes psychophysiological context as a covariate for case review and signal detection. Methods We performed an evidence map using scoping-review methods to characterize existing approaches at the intersection of (a) ICSR narrative standards and data quality management, (b) narrative analytics and natural language processing (NLP) for pharmacovigilance, and (c) psychophysiological/nocebo mechanisms relevant to adverse event (AE) reporting. We used PRISMA-ScR and JBI guidance to structure and report the search and charting processes.19,20 Information sources and search strategy We searched PubMed (MEDLINE), Europe PMC, OpenAlex and IEEE Xplore on 18 February 2026 for records published from 2005 onwards, using pre-specified queries combining pharmacovigilance/ICSR narrative concepts with NLP/signal detection/duplicate handling and psychophysiology/nocebo terms (full search strings and hit counts in Supplementary Appendix S1). We additionally screened the first 200 Google Scholar results (sorted by relevance) using a simplified query. Records were exported to Zotero, merged, and deduplicated; a second duplicate check was performed in Rayyan. In total, 966 records were identified (PubMed n=115; Europe PMC n=598; OpenAlex n=42; IEEE Xplore n=11; Google Scholar n=200). After deduplication in Zotero and Rayyan (306 duplicates removed), 660 unique records remained for title/abstract screening in Rayyan. We excluded 238 records at title/abstract and sought full texts for 422 records; 350 full texts were retrieved (72 not retrieved) and assessed for eligibility, resulting in 250 included sources and 100 full-text exclusions with documented reasons (Supplementary Figure S1; Supplementary Data S1). Eligibility, charting and synthesis We included empirical and methodological studies that (i) analyzed ICSR narratives or proposed narrative templates, (ii) applied NLP/text mining to safety report narratives with outcomes relevant to pharmacovigilance tasks, or (iii) connected nocebo/psychophysiological mechanisms to symptom reporting and misattribution. We also included authoritative standards and guidance documents (ICH, EMA, CIOMS). Each source was charted for (a) pharmacovigilance task(s), (b) the role of narrative vs. structured fields, (c) any explicit treatment of contextual determinants, and (d) implications for implementation (burden, governance, automation readiness). Full-text exclusions were documented using pre-specified reason categories. Results: evidence map Study selection and overview: Of 966 records identified, 660 unique records were screened and 350 full texts were assessed; 250 sources were included for charting (PRISMA flow in Supplementary Figure S1). Cluster sizes were: narrative analytics/NLP n=125 (50.0%), narrative data quality/standards n=87 (34.8%), operationalization of contextual determinants n=33 (13.2%), and nocebo/psychophysiology n=5 (2.0%). The full charting dataset and screening log are provided as Supplementary Data S1-S2. Cluster 1: narrative data quality as a determinant of pharmacovigilance performance Guidance documents consistently emphasize that narrative completeness is central for causality assessment and seriousness evaluation, particularly when structured fields are missing or inconsistent.1-4 Quantitative work such as vigiGrade demonstrates that the ‘quality’ of an ICSR is not only a function of coded fields but also of whether key narrative information is present, and highlights systematic patterns of missingness.13 These findings imply that small, well-designed prompts can shift data quality at scale. Cluster 2: narrative analytics and NLP in pharmacovigilance Multiple studies show that NLP applied to ICSR narratives can recover missing patient attributes (e.g., demographics) and extract clinically meaningful entities such as symptoms, time-to-onset, and drug–event links.15-17 Comparative work also suggests that text-mining can complement traditional signal detection approaches in specific use cases (e.g., medication errors).16 These results underscore that narrative fields are machine-readable at scale if the input text contains consistent cues. Cluster 3: expectancy/nocebo and psychophysiology as contributors to AE burden Across therapeutic areas, placebo-arm evidence indicates that AEs without pharmacologic exposure are common and frequently overlap with the adverse-event profiles of the active drug, supporting an expectancy-related (nocebo) component in symptom reporting.6-10 Psychophysiological models provide plausible mechanisms linking threat cues and stress to symptom expression via autonomic arousal and altered perception, and suggest that triggers, time course and symptom clustering can help distinguish contextual symptom formation from direct drug toxicity.11 Cluster 4: operationalization of contextual determinants We found no pharmacovigilance standard that encodes psychophysiological state or nocebo context as a structured ICSR element.1-4 Where discussed, contextual mechanisms are typically treated as narrative nuance rather than as information suitable for systematic capture or covariate adjustment. COPEDS is, to our knowledge, the only brief screening instrument explicitly designed to operationalize CPD-prone states in a way that could be summarized without specialist training.12 This creates an opportunity for a minimal narrative enhancement that remains compatible with current data models. Cross-cutting gaps relevant to drug safety Across clusters, three gaps recur. First, there is no standardized way to document contextual determinants (acute stressors, expectancy cues, CPD-like markers) at the time of symptom onset, even though these determinants may strongly influence symptom perception and reporting. Second, NLP and AI systems rarely treat such context as an explicit feature, in part because it is not captured consistently. Third, misclassification and duplicates can distort disproportionality analyses and signal triage, and current reporting guidance emphasizes the need to transparently describe analytic choices and heterogeneity.5,14,15,22 Methods proposal: a minimal context module in the ICSR narrative Building on the evidence map, we propose a simple, non-diagnostic enhancement to the case narrative: a short, COPEDS-informed micro-template plus a 3-level CPD flag. The design goals are to (i) capture psychophysiological context at symptom onset in a parsable form, (ii) remain fully compatible with E2B(R3) and current GVP guidance (no new core fields), (iii) impose minimal burden on reporters and safety staff, and (iv) be usable by both human assessors and NLP/AI systems. Figure 1. Conceptual pathway from contextual determinants to misclassification and signal noise (schematic). Placement within E2B(R3) and GVP The proposal does not modify the E2B(R3) data model. Instead, it standardizes a small header within existing free-text fields (e.g., ‘case narrative’, ‘reporter comments’), which are explicitly intended to convey clinical reasoning and contextual information.1-4 We suggest the following optional header: Table 1. Proposed minimal context micro-template for the ICSR narrative (example header elements). Triggering context What was happening in the hours/days before onset (medical, situational, informational)? Stroke unit admission; first exposure; read patient leaflet; media report about side effects Supports alternative explanations; improves time-anchoring; helps assess plausibility Expectancy cues Any explicit negative expectations or worries expressed before onset? “I was afraid I would get palpitations after reading about them.” Flags potential nocebo contribution; informs risk communication and follow-up Stress/autonomic markers Any acute stress response at onset (panic, hyperventilation, trembling, sweating)? Acute panic; hyperventilation; tremor; chills; nausea Differentiates autonomic arousal from pharmacologic toxicity; supports triage CPD-like markers (optional) Any transient cognitive-perceptual disruption? Derealization; tunnel vision; ‘not feeling real’; confusion; memory gaps Context covariate for analysis; may improve duplicate and NLP features Reporter rationale Why is the event suspected to be drug-related? Temporal association; dechallenge/rechallenge; alternative causes considered Preserves clinical reasoning; improves signal interpretability CPD flag definition (non-diagnostic covariate) To facilitate both manual review and automated analysis, we propose a simple 3-level CPD flag that reflects the likelihood of CPD-like phenomena during the episode, based either on a COPEDS screen or on clearly documented markers: Table 2. Proposed 3-level CPD flag as a non-diagnostic contextual covariate. 0 – Unlikely No CPD-like markers documented; COPEDS negative; symptoms explained by other context. Default if neither COPEDS nor narrative suggests CPD. 1 – Possible Some CPD-like markers or strong stress/expectancy context; uncertain temporal relation. Assign if ≥1 CPD marker OR strong expectancy/stress cues without clear CPD. 2 – Likely Multiple CPD-like markers and/or COPEDS positive; episode described as dissociation/confusion with autonomic arousal. Assign if COPEDS positive OR ≥2 CPD markers temporally linked to onset. Expected benefits, safeguards and governance At the case level, structured context prompts encourage reporters and assessors to consider psychophysiological and expectancy-related explanations alongside pharmacologic hypotheses, reducing premature attribution without dismissing the patient’s experience. At the system level, the context module can be used to stratify sensitivity analyses (e.g., separate analyses for CPD-likely cases), refine duplicate detection (clusters with similar contextual descriptors), and extend NLP feature sets with human-interpretable cues.12-18 Safeguards are essential. The CPD flag must be treated as a context cue, not as a causality label or a ‘psychogenic’ dismissal. Implementation should include standardized training examples, monitoring of inter-rater agreement, audit of downstream decision impacts, and explicit governance aligned with CIOMS guidance for AI use in pharmacovigilance.21 Discussion Our core clinical message is a drug-safety message: if contextual symptom formation is not recognized and documented at the point of reporting, pharmacovigilance systems can misclassify events, create spurious associations, and divert scarce expert review time from true signals.5,13-15 Because signal detection methods are sensitive to heterogeneity, transparently capturing contextual determinants as covariates can improve interpretability and robustness, especially when paired with reporting standards such as READUS-PV.22 Limitations This paper provides an evidence map and a methods proposal; it does not present new primary data. COPEDS was developed in psychotherapy-adjacent clinical samples, and its transportability to broader medical populations and across languages requires validation before global pharmacovigilance adoption.12 The proposed micro-template must also be tested for feasibility, acceptability and unintended consequences (e.g., stigmatizing interpretations or differential reporting). Research and implementation agenda We recommend pilot implementations in selected settings (e.g., hospital pharmacovigilance units, vaccine safety surveillance) with prospective evaluation of reporter burden, completeness metrics (e.g., vigiGrade-like measures), inter-rater agreement for the CPD flag, and impact on duplicate handling and stratified disproportionality analyses.13-15,22 Table 3. Implementation and evaluation checklist for the context module. Workflow Embed the micro-template in existing narrative fields (no schema change). Provide a one-page user guide and exemplar cases. Completion rate; time to complete; reporter acceptability; missingness by report type. Training Short training for case processors and clinicians: differentiating context cues vs causality labels; examples and ‘do-not-misuse’ guidance. Inter-rater agreement for CPD flag (κ); audit of misclassification complaints; feedback loops. Analytics Use CPD flag as a covariate for sensitivity analyses (e.g., stratified disproportionality) and as a feature in duplicate detection / NLP. Signal robustness across strata; change in false-positive triage burden; duplicate clustering performance. Governance Define governance aligned with CIOMS guidance: documentation, model monitoring, and bias/stigma risk assessment for any AI use. Governance checklist completion; adverse impact monitoring; transparency reporting. Conclusions Psychophysiological and expectancy-related mechanisms are well documented, yet remain largely invisible in current pharmacovigilance data structures.6-10 A minimal context module in ICSR narratives—paired with a non-diagnostic CPD flag—offers a feasible step toward context-rich pharmacovigilance that can reduce misclassification and improve signal interpretability while remaining compatible with E2B(R3) and GVP workflows.1-4 Plain Language Summary Adverse event reports are a key source of information used to monitor the safety of medicines after they are marketed. However, the free-text narratives in these reports often miss important context about what was happening when symptoms started. Some symptoms can be influenced or amplified by negative expectations (nocebo effects) and by acute stress or autonomic arousal. If this context is not documented, pharmacovigilance teams may misclassify events as drug reactions, which can create ‘false alarms’ and make true safety signals harder to detect. In this paper, we propose a simple, low-burden way to capture context within existing E2B(R3) narrative fields: (1) a short set of prompts (micro-template) for triggers, expectancy cues, and stress-related markers, and (2) an optional 3-level flag indicating whether cognitive-perceptual disruption (CPD-like) features were unlikely, possible, or likely during the episode. The CPD flag is intended as a non-diagnostic context cue and must not be used to dismiss patient experiences. The goal is to support better case assessment, reduce noise in signal detection, and enable more interpretable NLP/AI analyses. Declarations Ethics approval: Not applicable (no new human data were collected). Funding: None declared. Competing interests: None declared. Data availability: The search log, PRISMA flow numbers, screening log, and evidence map charting dataset are available in the supplementary material of this article. Use of AI: AI tools were used to support literature search, screening and data charting assistance, and language editing. All content, citations, and eligibility decisions were reviewed and verified by the author. Author contributions: EN conceived the paper, performed the evidence mapping, and drafted the manuscript. References 1. International Council for Harmonisation (ICH). Implementation Guide for Electronic Transmission of Individual Case Safety Reports (ICSRs): E2B(R3) Data Elements and Message Specification (Step 3, 18 July 2025). 2. European Medicines Agency. Guideline on good pharmacovigilance practices (GVP) – Module VI: Collection, management and submission of reports of suspected adverse reactions to medicinal products (Rev 2, 28 July 2017). 3. European Medicines Agency. 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Artificial Intelligence in Pharmacovigilance. Geneva: CIOMS; 2025. doi:10.56759/cdob6397. 22. Fusaroli M, Poluzzi E, Raschi E, et al. The Reporting of a Disproportionality Analysis for Drug Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Development and Statement. Drug Saf. 2024;47:575-584. doi:10.1007/s40264-024-01421-9. Information & Authors Information Version history V1 Version 1 24 February 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords adverse event reporting copeds e2b(r3) individual case safety reports misclassification narrative nocebo pharmacovigilance psychophysiology signal detection Authors Affiliations Eik Niederlohmann 0009-0005-6803-3399 [email protected] Kliniken Erlabrunn gGmbH View all articles by this author Metrics & Citations Metrics Article Usage 187 views 63 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Eik Niederlohmann. 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