Repetitive transcranial electromagnetic stimulation, RTMS, does improve fatigue, depression and cerebral symptoms in severe Long COVID syndrome.
Bibliographic record
Abstract
Long COVID is characterized by severe fatigue after exertion, stress intolerance, lack of concentration, and cerebral impairment more than 12 weeks after SARS-CoV-2 infection. 6,5% of the infected are involved. An effective therapy is not available. Repetitive transcranial electromagnetic Stimulation, RTMS, is a noninvasive treatment with minor side effects to selectively stimulate or suppress selected cerebral regions. We applied RTMS to patients with long COVID syndrome. Methods: 7 patients (6f, 1m) who remained severely compromised and unable to work because of fatigue syndrome according to the Canadian criteria (Carruthers BM 2003) were treated with 10Hz RTMS on the left dorsolateral prefrontal cortex for 20 sessions within 4 weeks. Results: The Canadian Criteria of fatigue showed a consistent improvement in fatigue in the 7 patients by 24%, 14%, 35%, 53%, 24%, 8% and 37%, mean 27,8%. The Fatigue Severity Scale (Krupp, Arch Neurol, 1989) improved importantly by 16,4 points. Bell Score (Bell 1995), measuring severity of fatigue, improved by 25 points. In Beck´s inventory II (Hautzinger et al, 2007), the depression score decrease from 33 points, severe depression, to 17 points, reflecting only minor depression after RTMS. Conclusion: Repetitive transcranial electromagnetic stimulation, RTMS, on the left dorsolateral prefrontal cortex caused an important improvement of severe Long COVID symptoms in 6 of 7 patients. A randomized study is necessary. Until then RTMS should be offered to Long COVID patients in face of the lack of any treatment, of minor side effects and the consistent effects in our study.
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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 | 0.000 |
| 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) | 0.002 | 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".