Cerebral function imaging of acupuncture treatment for stroke: A protocol for systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background: Stroke is one of the most common causes of death and is the main cause of persistent and acquired disability in adults worldwide. Acupuncture is recommended as an alternative and complementary strategy for stroke treatment by the World Health Organization as it can significantly improve patients' quality of life. However, the central nervous system (CNS) mechanism of acupuncture treatment of stroke is unclear. The aim of this study is explore the effective pathway and action mechanism of acupuncture treatment for stroke on the CNS.Methods: The following databases will be searched by electronic methods: PubMed; Medline; Embase; Cochrane Library; Chinese National Knowledge Infrastructure; VIP Database; Wan-fang Data; Chinese Biomedical Database. All of them will be retrieved from the establishment date of the electronic database to December 2020, all included studies will be evaluated risk of bias by the Cochrane Handbook. Spatial coordinates of the Montreal Neurological Institute of activated brain regions will be the primary outcome. The systematic review will be conducted with the use of SDM v5.141 software for voxel meta-analysis in this study.Results: This study will obtain the correlation between the activated brain regions of acupuncture treatment for stroke.Conclusion: This study will explore the effective pathway and action mechanism of acupuncture, and provide a reliable scientific basis for the treatment of stroke by acupuncture.PROSPERO registration number: CRD42021231329.
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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 | 0.042 | 0.063 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.020 | 0.024 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.003 |
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".