Simple acupuncture combined with fluoxetine in the treatment of poststroke depression
Bibliographic record
Abstract
BACKGROUND: Poststroke depression is a common secondary mental disorder after stroke, which increases the recurrence rate and mortality rate after stroke and hinders the recovery of function. As a combination therapy, simple acupuncture combined with fluoxetine has achieved good clinical effect, but there is a lack of evidence-based medicine. The purpose of this study is to evaluate the efficacy and safety of acupuncture combined with fluoxetine in the treatment of poststroke depression by meta-analysis. METHODS: Search Chinese and English databases: China national knowledge infrastructure, VP information Chinese Journal Service Platform, Wanfang, the China Biomedical Database, PubMed, Embase, the Cochrane Library, and web of science. A randomized controlled trial of simple acupuncture combined with fluoxetine in the treatment of poststroke depression will be selected. The retrieval time is of the establishment of the database in January 2021. Selected literature is extracted and deleted by 2 researchers, and the quality of the included literature is evaluated. The included literature is analyzed by Meta with RevMan5.3 software. RESULTS: In this study, the efficacy and safety of acupuncture combined with fluoxetine in the treatment of post-stroke depression are evaluated by Hamilton Depression scale (HAMD) and its reduction rate, Treatment Emergency Symptom Scale, Self-rating Depression Scale, and Activities of Daily living scale. CONCLUSION: This study will provide reliable evidence-based evidence for the clinical application of acupuncture combined with fluoxetine in the treatment of post-stroke depression. OSF REGISTRATION NUMBER: DOI 10.17605/OSF.IO/5J896.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".