Auricular acupressure approach for the early stage of knee osteoarthritis
Bibliographic record
Abstract
We read with great interest a randomized, sham-controlled pilot trial by Zhang et al.1 on the auricular acupressure approach to treat the early stage of knee osteoarthritis. The authors showed that the intervention group had a greater improvement in the visual analog scale and the Western Ontario and McMaster Universities Arthritis Index on Days 3 and 7, and had a lower frequency of celebrex use compared to the control group,1 providing a practical modality for the treatment of knee osteoarthritis. However, we would like to address some concerns about this study. First, although there were no significant differences in baseline characteristics between the intervention and control groups, a mild imbalance can be observed in some baseline variables (such as age, sex and Kellgren–Lawrence grade) between the intervention and control groups. Differences in these variables may not reach statistical significance due to the small sample size in this study. Data on comorbidities and occupation are also not recorded, and some of them, especially concomitant rheumatic diseases and work with a heavy physical workload or repetitive knee bending,2–4 may have a potential confounding effect on the study results. We suggest that authors can perform a sensitivity analysis using the propensity score method to control or adjust baseline covariates. Second, people who underwent intra-articular injections during the previous 3 months were excluded from the study, but the effectiveness of hyaluronic acid and platelet-rich plasma for intra-articular injections has been shown for more than 6 months.4,5 Furthermore, information on weight change, lifestyle, physical activities, nutritional supplements and adjuvant medical aid of participants is not evaluated during the study period,4 which could also affect the study results. These issues need to be made more clear. Finally, we appreciate the work of Zhang and colleagues and look forward to their responses. Shiuan-Chih Chen (Conceptualization [equal], Investigation [equal], Methodology [equal], Project administration [equal], Validation [equal], Writing—original draft [equal], Writing—review & editing [equal]), Chin-Feng Tsai (Conceptualization [equal], Investigation [equal], Methodology [equal], Validation [equal], Writing—original draft [equal], Writing—review & editing [equal]), Po-Hui Wang (Conceptualization [equal], Investigation [equal], Methodology [equal], Validation [equal], Visualization [equal], Writing—original draft [equal], Writing—review & editing [equal]), Yuan-Ti Lee (Conceptualization [equal], Investigation [equal], Methodology [equal], Project administration [equal], Supervision [equal], Validation [equal], Visualization [equal], Writing—original draft [equal], Writing—review & editing [equal]), and Chun-Chieh Chen (Conceptualization [equal], Investigation [equal], Methodology [equal], Project administration [equal], Supervision [lead], Validation [equal], Visualization [equal], Writing—original draft [equal], Writing—review & editing [lead]) None declared.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".