Bibliographic record
Abstract
This article identifies and comments on published studies of acupuncture treatment for hypertension and stroke. In all, 27 appropriate papers were analysed: 13 were hypertension papers with 3 being of controlled trials, and 14 were stroke papers with 5 controlled trials. The articles consisted of randomised controlled trials (RCTs), controlled trials, and case series studies. The hypertension papers also involved some cohort studies. In general, sample sizes of these studies were small. Acupuncture in these papers was used as a secondary intervention for treating hypertension and as a tertiary intervention for stroke rehabilitation. The RCTs (the top of the hierarchy of evidence) of hypertension showed that acupuncture was not more effective than sham acupuncture or the anti-hypertensive drug, reserpine; however, all the case series suggested that acupuncture was an effective treatment. The RCT evidence for stroke showed that the effectiveness of acupuncture was similar to that of conventional treatment. The conclusion was that from the papers analysed there is insufficient evidence to show that acupuncture produces better results than other treatments for hypertension or stroke.
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 | 0.017 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".