Acupuncture treatment for knee osteoarthritis with sensitive points: protocol for a multicentre randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: There is a lack of curative medical treatment for patients with knee osteoarthritis (KOA). Acupuncture represents an important alternative therapy. According to the theory of traditional Chinese medicine and preliminary clinical evidence, the patients' acupoints and tender points may become sensitised when the body suffers from a disease state; stimulation of such sensitive points could lead to a disease improvement. It is thus hypothesised that acupuncture at highly sensitised points on patients with KOA would achieve better treatment outcomes than acupuncture at low/non-sensitised points. Previously, we conducted a pilot trial to prove the feasibility of further investigation. METHODS AND ANALYSIS: A three-arm, parallel, multicentre randomised controlled trial of 666 patients will be conducted at four hospitals of China. Eligible patients with KOA who consent to participate will be randomly assigned to a high-sensitisation group (patients receive acupuncture treatment at high-sensitive points), a low/non-sensitisation group (patients receive acupuncture treatment at low/non-sensitive points) or a waiting-list group (patients receive standard acupuncture treatment after the study is concluded) via a central randomisation system using 1:1:1 ratio. The primary outcome is the change of Western Ontario and McMaster Universities Osteoarthritis Index total score from baseline to 16 weeks. Outcome assessors and data analysts will be blinded and participants will be asked not to reveal their allocation to assessors. The outcome analyses will be performed both on the intention-to-treat and per-protocol population. The primary analyses will test if acupuncture at highly sensitised points would achieve statistically better treatment outcomes than acupuncture at low/non-sensitised points and no acupuncture (ie, waiting list), respectively. A small number of prespecified subgroup analyses will be conducted. ETHICS AND DISSEMINATION: Ethics approval has been granted by the Bioethics Subcommittee of West China Hospital, Sichuan University: 2017 (Number 228). Results will be expected to be published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT03299439.
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 | 0.047 | 0.038 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.079 | 0.012 |
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".