[Effect of <i>Jiannao Anshen</i> acupuncture on insomnia: a functional magnetic resonance imaging study].
Bibliographic record
Abstract
OBJECTIVE: acupuncture (acupuncture for brain-invigorating and mind-calming) on insomnia, and the dynamic changes of functional magnetic resonance imaging (fMRI) before and after acupuncture, to provide neuroimaging basis of acupuncture for insomnia. METHODS: acupuncture at Baihui (GV 20), bilateral Sishencong (EX-HN 1), Fengfu (GV 16), Fengchi (GB 20), Qiangjian (GV 18), occipital sideline, Anmian (Extra), Yiming (EX-HN 14) and Dazhui (GV 14); treatment was given once a day, 10 days as a course of treatment, and a total of 2 courses of treatment was given. The resting-state fMRI data was collected in the observation group before and after treatment. The participants in the control group received no treatment, and the resting-state fMRI data was collected only once. The resting-state fMRI data in the observation group before treatment and in the control group, also the observation group before and after treatment were compared. The Pittsburgh sleep quality index (PSQI), insomnia severity index (ISI), daytime insomnia symptom scale (DISS), hyperarousal scale (HAS), Hamilton anxiety scale (HAMA), Hamilton depression scale (HAMD), digit symbol test, mini-mental state examination (MMSE), Montreal cognitive assessment (MoCA) and the A, B speed of trail making test (NCT) were observed in the observation group before and after treatment. The clinical efficacy of the observation group was evaluated. RESULTS: <0.05). CONCLUSION: acupuncture could effectively improve the sleep quality in patients with insomnia, and acupuncture could enhance the connection of left dorsal, right ventral and left ventral caudate nucleus with brain area.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".