Brain function assessment of acupuncture for chronic insomnia disorder with mild cognitive dysfunction based on fNIRS: protocol for a randomized controlled trial
Bibliographic record
Abstract
Background: Chronic Insomnia Disorder (CID) is highly prevalent among older adults and impairs cognitive function. Insomnia accelerates the progression of mild cognitive impairment (MCI) and increases the risk of developing dementia. Acupuncture has been demonstrated in improving sleep quality and cognitive function. This study aims to explore the functional brain characteristics of CID with MCI patients and to assess the effects of acupuncture therapy using functional near-infrared spectroscopy (fNIRS). Methods and design: This study is a single-center randomized controlled trial. Participants will be randomly assigned to the manual acupuncture group or the placebo acupuncture group for an 8-week intervention period. fNIRS data will be collected during resting test and working memory test at baseline and at end of the intervention. The primary outcome is the change of the Montreal Cognitive Assessment (MoCA) score, secondary outcomes include the change of Mini-Mental State Examination (MMSE), Insomnia Severity Index (ISI), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder Scale (GAD-7), and Apathy Evaluation Scale-Informant (AES-I). Discussion: The results of the study will provide insights into the effects of acupuncture on sleep quality and cognitive performance in CID with MCI patients. By utilizing fNIRS technology, we will elucidate the neural functional characteristic underlying the therapeutic benefits of acupuncture. Clinical trial registration: https://ClinicalTrials.gov, identifier ChiCTR2300076182.
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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 | 0.019 | 0.016 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.057 | 0.009 |
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".