Clinical efficacy and safety of warm acupuncture in the treatment of type 2 diabetic kidney disease: A protocol of a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetic kidney disease (DKD) is one of the most common and harmful chronic complications in clinical practice, and there is no reliable and targeted treatment plan at present. As a classic complementary and alternative therapy, evidence have shown that warm acupuncture has advantages in the treatment of type 2 DKD. However, there is still a lack of high-quality and long-term follow-up randomized controlled trials of warm acupuncture in the treatment of type 2 DKD. METHODS: This is a prospective randomized controlled trial to investigate the efficacy and safety of warm acupuncture in the treatment of type 2 DKD. Participants will be randomly assigned in a 1:1 ratio to either the treatment group (treated with conventional Western medicine) or the control group (treated with warm acupuncture added on the basis of the control group). Both groups will receive 12 weeks of treatment followed by 24 weeks of follow-up. Observation indicators include: 24-hour urinary protein quantification, kidney function, TCM syndrome score and adverse reactions. Finally, SPSS21.0 software will be used to analyze the data. DISCUSSION: This study will evaluate the efficacy and safety of warm acupuncture in the treatment of DKD, and the results of this trial will provide clinical evidence for the treatment of type 2 DKD. TRIAL REGISTRATION: The TCTR identification number is TCTR20221104004.
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 | 0.036 | 0.033 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.038 | 0.006 |
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".