Utilizing the DMN and DAN to Study the Effects of Acupuncture on Patients with Cognitive Impairment in Long COVID: A Pragmatic Randomized Controlled Trial Protocol
Bibliographic record
Abstract
Background: Cognitive impairment is one of the long COVID symptoms that many people experience after Coronavirus Disease 2019 (COVID-19). Many individuals report a decline in cognitive functions, such as reduced memory and brain fog. These symptoms not only directly affect the cognitive functions of the brain but also hinder daily living activities, thereby reducing the quality of life. Moreover, these symptoms are significant risk factors for long-term cognitive decline in the elderly and can have both short-term and long-term effects on brain function. Clinically, acupuncture is widely used to improve cognitive impairment in the elderly. Elucidating the brain network mechanisms underlying acupuncture therapy for long COVID-related cognitive impairment represents an urgently needed research focus. In this study, we employed acupuncture as an intervention to mitigate cognitive decline in long COVID patients and investigate the potential mechanisms by which acupuncture alleviates cognitive impairment. Methods: In this randomized controlled trial, 60 eligible participants are planned to be recruited and randomly assigned in a 1:1 ratio to the acupuncture group and the health education group, which will then receive acupuncture treatment and health education. The acupuncture group will participate in treatment three times per week for a total of 8 weeks. The health education group will receive health education once per week for a total of 8 weeks. The primary assessment index was the Montreal Cognitive Assessment Scale (MoCA), and the secondary assessment indexes included Clinical Dementia Rating (CDR), Mini-Mental State Examination (MMSE), Activity of Daily Living Scale (ADL), Auditory Verbal Learning Test-Huashan Version (AVLT-H), and resting-state functional magnetic resonance imaging data. These assessment indicators were all tested in 1 week each before and after the intervention was implemented. Discussion: This trial aims to investigate the therapeutic effects of acupuncture on cognitive impairment in patients with long COVID and to further explore the imaging mechanisms by which acupuncture alleviates cognitive dysfunction in these patients. .
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 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".