Acupuncture for insomnia? An overview of systematic reviews
Bibliographic record
Abstract
BACKGROUND: Several systematic reviews of acupuncture as a treatment of insomnia have recently emerged. Their results are far from uniform. AIM: To summarize and critically evaluate these reviews with a view of defining the reasons for their discrepant conclusions and providing an overall verdict about the therapeutic value of acupuncture for insomnia. METHODS: Thirteen electronic databases (Medline, Embase, Amed, CINHAL, Health Technology Assessments, DARE, Cochrane, six Korean/Chinese databases) were searched for relevant articles and data from the included reviews were extracted according to pre-defined criteria. Their methodological quality was assessed using the 'Overview Quality Assessment Questionnaire'. RESULTS: Ten systematic reviews of acupuncture for insomnia were published between 2003 and 2010. They differed in numerous respects. Several reviews draw strongly positive conclusions. Owing to these several caveats, the best evidence is, however, not clearly positive. CONCLUSION: The evidence for acupuncture as a treatment of insomnia is plagued by important limitations, e.g. the poor quality of most primary studies and some systematic reviews. Those that are sensitive to such limitations, fail to arrive at a positive verdict about the effectiveness of acupuncture.
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 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 | 0.021 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.025 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| 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".