Comparative Analysis of Long COVID and Post-Vaccination Syndrome: A Cross-Sectional Study of Clinical Symptoms and Machine Learning-Based Differentiation
Bibliographic record
Abstract
ABSTRACT Importance Long COVID is a well-documented post-viral syndrome, while post-vaccination syndrome (PVS) remains poorly characterized. Understanding their similarities and differences is essential for refining diagnostic criteria and developing targeted interventions. This study systematically compares the symptomatology of long COVID and PVS following COVID-19 vaccination, highlighting key distinctions that could inform clinical practice and research. Objective To assess the clinical characteristics of long COVID and PVS and identify key distinguishing features between the conditions. Design, Setting and Participants This cross-sectional analysis used questionnaire data from the decentralized Yale Listen to Immune, Symptom and Treatment Experiences Now (LISTEN) Study, collected from May 2022 to July 2023. Data analysis occurred between July 2023 and May 2024. A convenience sample of adults (age ≥18 years) with either long COVID or PVS was included. Main Outcomes and Measures Symptom data were analyzed using clustering techniques to identify groups with shared symptom patterns. A gradient-boosted machine learning model was used to determine the most distinguishing symptoms between long COVID and PVS. Results The long COVID group (n = 441) and PVS group (n = 241) had similar demographic profiles (median age 46 years; 74% vs 80% female, respectively). Participants with long COVID most commonly reported brain fog, altered sense of smell and taste, shortness of breath, fatigue, memory problems, and difficulty speaking. Participants with PVS more frequently reported burning sensations, neuropathy, and numbness. Clustering analysis identified three symptom-based subgroups: one enriched for neurological symptoms and PVS; one characterized by multi-system symptoms and predominantly long COVID; and one dominated by psychiatric and sleep symptoms, also primarily long COVID. The machine learning model achieved an AUC of 0.79 (95% CI, 0.75–0.82) and highlighted altered sense of smell, cough, burning sensations, and brain fog as key differentiators. Conclusions and Relevance Although long COVID and PVS share overlapping symptoms, they have distinct clinical profiles, suggesting the possibility of different underlying biological mechanisms. These distinctions may help refine diagnostic criteria, guide personalized treatment strategies, and inform further research into their respective pathophysiology. KEY POINTS Question What are the similarities and differences between long COVID and post-vaccination syndrome (PVS)? Findings In this cross-sectional study of 682 individuals, machine learning models identified distinct symptoms between long COVID and PVS. Long COVID was characterized by brain fog, altered sense of smell, and shortness of breath, while PVS was associated with burning sensations, neuropathy, and numbness. Meaning Although long COVID and PVS share overlapping symptoms, they have distinctive symptom profiles, suggesting potentially different underlying biological mechanisms. Understanding these differences can guide clinical diagnosis and targeted management, and inform further research into their distinct immune and biological pathways.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".