Long-COVID autonomic syndrome in working age and work ability impairment
Bibliographic record
Abstract
Long-COVID19 has been recently associated with long-sick leave and unemployment. The autonomic nervous system functioning may be also affected by SARS-CoV-2, leading to a chronic autonomic syndrome. This latter remains widely unrecognized in clinical practice. In the present study, we assessed the occurrence of Long-COVID19 Autonomic Syndrome in a group of active workers as well as the relationships between their autonomic dysfunction and work ability. This prospective observational study was conducted during the 2nd wave of the pandemic in Italy. Forty-five patients (53.6 ± 8.4 years; 32 M) hospitalized for COVID19, were consecutively enrolled at the time of their hospital discharge (T0) and followed-up for 6 months. Autonomic symptoms and work ability were assessed by COMPASS31 and Work Ability Index questionnaires at T0, one (T1), three and six (T6) months after hospital discharge and compared to those retrospectively collected for a period preceding SARS-CoV-2 infection. Clinical examination and standing test were also performed at T1 and T6. One in three working-age people developed a new autonomic syndrome that was still evident 6 months after the acute infection resolution. This was associated with a significant reduction in the work ability. Recognition of Long-COVID19 Autonomic Syndrome may promote early intervention to facilitate return to work and prevent unemployment.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".