Scalp acupuncture and computer assisted cognitive rehabilitation for stroke: A meta-analysis of randomised controlled trials
Bibliographic record
Abstract
Objective: To assess the clinical effectiveness of scalp acupuncture and computer assisted cognitive rehabilitation in the treatment of cognitive impairment in stroke patients. Methods: The literatures published before August 2021 in the following databases were included: PubMed, Chinese Biomedical Database, Wanfang Database, China National Knowledge Infrastructure, Database of Chinese sci-tech periodicals (VIP), EBSCO Information Services, MEDLINE and Web of Science. Only randomised controlled trials (RCTs) were included. Primary outcomes were the Loewenstein Occupational Therapy Cognitive Assessment (LOTCA) and Montreal Cognitive Assessment (MoCA). Our secondary outcome was Modified Barthel Index Score (MBI). The quality of all included trials was evaluated according to the Cochrane Collaboration. This protocol was registered in PROSPERO (CRD42016048528). Results: Sixteen articles were selected including 1333 patients. The result of the meta analysis showed that the combination of scalp acupuncture and computer assisted cognitive rehabilitation had a significant improvement in the cognitive impairments. The analysis of LOTCA showed the improvement on the LOTCA (p < 0.0001, n = 410, I2 = 86%, mean difference 8.31). The meta-analysis of the MOCA showed a weighted mean difference of 3.76 and 95% confidence intervals (CI) of 2.90-4.62 (p < 0.0001, n = 301). Besides, it was showed that the combination therapy played an important role in the improvement of the score of MBI with a weighted mean difference of 9.30 and 95% confidence intervals (CI) of 5.87-12.672 (p < 0.0001, n = 278). Conclusions: Scalp acupuncture and computer assisted cognitive rehabilitation appears to be effective for stroke patients with respect to certain outcomes. However, the evidence thus far is inconclusive. Further high-quality RCTs following standardized guidelines with a low risk of bias are needed to confirm the effectiveness of acupuncture for postpartum depression.
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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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.043 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".