Auricular acupressure approach for the early stage of knee osteoarthritis
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».