High Somatization Rates, Frequent Spontaneous Recovery, and a Lack of Organic Biomarkers in Post‐Covid‐19 Condition
Bibliographic record
Abstract
INTRODUCTION: Many patients report neuropsychiatric symptoms after SARS-CoV-2 infection. Data on prevalence of post-COVID-19 condition (PCC) vary due to the lack of specific diagnostic criteria, the report of unspecific symptoms, and reliable biomarkers. METHODS: PCC patients seen in a neurological outpatient department were followed for up to 18 months. Neurological examination, SARS-CoV-2 antibodies, Epstein-Barr virus antibodies, and cortisol levels as possible biomarkers, questionnaires to evaluate neuropsychiatric symptoms and somatization (Patient Health Questionnaires D [PHQ-D]), cognition deficits (Montreal Cognitive Assessment [MoCA]), sleep disorders (ISS, Epworth Sleepiness Scale [ESS]), and fatigue (FSS) were included. RESULTS: A total of 175 consecutive patients (78% females, median age 42 years) were seen between May 2021 and February 2023. Fatigue, subjective stress intolerance, and subjective cognitive deficits were the most common symptoms. Specific scores were positive for fatigue, insomnia, and sleepiness and were present in 95%, 62.1%, and 44.0%, respectively. Cognitive deficits were found in 2.3%. Signs of somatization were identified in 61%, who also had an average of two symptoms more than patients without somatization. Overall, 28% had a psychiatric disorder, including depression and anxiety. At the second visit (n = 92), fatigue (67.3%) and insomnia (45.5%) had decreased. At visit three (n = 43), symptom load had decreased in 76.8%; overall, 51.2% of patients were symptom-free. Biomarker testing did not confirm an anti-EBV response. SARS-CoV-2-specific immune reactions increased over time, and cortisol levels were within the physiological range. CONCLUSION: Despite high initial symptom load, 76.8% improved over time. The prevalence of somatization and psychiatric disorders was high. Our data do not confirm the role of previously suggested biomarkers in PCC patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".