The Long-COVID autonomic syndrome in hospitalized patients: A one-year prospective cohort study
Bibliographic record
Abstract
Long-COVID syndrome is characterized by fatigue, orthostatic intolerance, tachycardia, pain, memory difficulties, and brain fog, which may be associated with autonomic nervous system abnormalities. We aimed to evaluate the short and long-term course of COVID-19 autonomic symptoms and quality of life (QoL) after SARS-CoV-2 infection through a one-year follow-up combined with validated questionnaires. Additionally, we aimed to identify patients with worsening autonomic symptoms at 6 and 12 months by dividing the patient cohort into two sub-groups: the Post-COVID healed Control sub-group (total score<16.4) and the Long-COVID autonomic syndrome sub-group (total score>16.4). This prospective cohort studied 112 SARS-CoV-2 positive patients discharged from Humanitas Research Hospital between January and March 2021. Autonomic symptoms and QoL were assessed using the composite autonomic symptom scale 31 (COMPASS-31) and Short Form Health Survey (SF-36) questionnaires at various time points: before SARS-CoV-2 infection (PRE), at hospital discharge (T0), and at 1 (T1), 3 (T3), 6 (T6), and 12 (T12) months of follow-up. COMPASS-31 total score, Orthostatic Intolerance and Gastrointestinal function indices, QoL, physical functioning, pain, and fatigue scores worsened at T0 compared to PRE but progressively improved at T1 and T3, reflecting the acute phase of COVID-19. Unexpectedly, these indices worsened at T6 and T12 compared to T3. Subgroup analysis revealed that 47% of patients experienced worsening autonomic symptoms at T6 and T12, indicating Long-COVID autonomic syndrome. Early rehabilitative and pharmacological therapy is recommended for patients at the T1 and T3 stages after SARS-CoV-2 infection to minimize the risk of developing long-term autonomic syndrome.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".