rTMS for post-covid-19 condition: A sham-controlled case series involving iTBS-300 and iTBS-600
Bibliographic record
Abstract
Post-Covid-19 Condition (PCC) is a syndrome comprised of symptoms persisting 3 months or more beyond SARS-CoV-2 primary infection. It is typically characterized by fatigue, cognitive problems and psychiatric symptoms, as well as cardiac symptoms that contribute to exercise intolerance in many. Despite the high prevalence of PCC among those with a prior SARS-CoV-2 infection, there is currently no widely accepted rehabilitation strategy, and many conventional modalities are movement-based. Non-invasive brain stimulation methods such as repetitive transcranial magnetic stimulation (rTMS) may have some potential to alleviate the cognitive and affective symptoms of PCC without reliance on exercise. The purpose of the present study was to explore the feasibility and tolerability of using rTMS to treat symptoms of "brain fog" and affective disturbance among those living with PCC, using a case series design. We enrolled four individuals with PCC following a confirmed SARS-CoV-2 infection, at least 3 months after the resolution of the primary infection. Participants were randomized to 4 sessions of active and 2 sessions of sham intermittent theta-burst stimulation (iTBS); two intensities of iTBS were evaluated: iTBS-300 and iTBS-600. No adverse events occurred in active or sham stimulation; 2 participants reported tingling sensation on the scalp but no other tolerability issues. Trends in symptoms suggested improvements in cognitive interference, quality of life, and anxiety in the majority of participants. In summary, in this case series iTBS was well tolerated among 4 individuals with PCC; active stimulation was associated with positive trends in some primary symptom clusters as compared with sham stimulation. Future studies should examine the effects of iTBS on PCC symptoms in the context of experimental studies and randomized controlled trials.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".