Vagus Nerve Stimulation for the Treatment of Post–COVID-19 Condition
Bibliographic record
Abstract
Horizon Scan reports provide brief summaries of information regarding new and emerging health technologies; Health Technology Update articles typically focus on a single device or intervention. These technologies are identified through the CADTH Horizon Scanning Service as topics of potential interest to health care decision-makers in Canada. This Horizon Scan summarizes the available information regarding vagus nerve stimulation for the treatment of post–COVID-19 condition, also known as long COVID. In particular, the Horizon Scan discusses the Dolphin Neurostim, the first medical device to receive a Health Canada emergency authorization for expanded use in post–COVID-19 conditions. The emerging evidence from early findings of small pilot studies suggests that vagus nerve dysfunction may be implicated in some post–COVID-19 conditions and neurostimulation of the vagus nerve could help improve some symptoms. However, the evidence is limited because the studies were not powered to detect statistically significant differences in outcomes, and it is unclear if the reported findings were clinically meaningful. Due to the heterogeneity of post–COVID-19 condition and limited understanding of its pathophysiology, the extent of vagus nerve dysfunction’s involvement in the condition is unclear. Where that dysfunction is implicated, electrical stimulation of the vagus nerve may be a potentially useful therapeutic option, likely complementary to other treatment options. Emerging evidence from ongoing and future research could help define the clinical effectiveness of the technology and guide its appropriate use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".