[Effects of <i>Huayu Tongluo</i> moxibustion on cognitive function and insulin resistance in patients with type 2 diabetes mellitus and cognitive decline: a randomized controlled trial].
Bibliographic record
Abstract
Objective: (transforming stasis and unblocking collaterals) moxibustion on cognitive function and insulin resistance in patients with type 2 diabetes mellitus (T2DM) and cognitive decline. Methods: moxibustion at Baihui (GV20), Shenting (GV24), and Dazhui (GV14). Pressing moxibustion was applied to Baihui (GV20) for 20 min, while suspended moxibustion was applied to Shenting (GV24) and Dazhui (GV14) for 20 min each. Treatments of moxibustion were administered every other day (three times per week) for 12 weeks. All patients were followed up for 12 weeks, during which their original hypoglycemic medication regimen was maintained. Before treatment, after 12 weeks of treatment, and at the 12-week follow-up, the scores of Montreal cognitive assessment (MoCA), mini-mental state examination (MMSE), Addenbrooke's cognitive examination Ⅲ (ACE-Ⅲ), symbol digit modalities test (SDMT), and Athens insomnia scale (AIS) and the insulin resistance index (HOMA-IR) were observed in the two groups. Results: <0.05). Conclusion: moxibustion can effectively improve cognitive function in patients with T2DM and cognitive decline. This improvement may be associated with the reduction in insulin resistance.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".