Comparative Analysis of Long COVID and Post-Vaccination Syndrome: A Cross-Sectional Study of Clinical Symptoms and Machine Learning-Based Differentiation
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».