関連する実験動画
Updated: May 10, 2026

07:34
Manual Therapy for a Chronic Non-Specific Low Back Pain Rat Model
Published on: August 11, 2023
788
ランダム化制御試験における非特異性頸部痛に対する対応介入の報告の質と試験結果との関連:体系的なレビューの二次分析
Paolo Mastromarchi1, Stephen May2, Nancy Ali2
1Fisiostudio, via Gentilino, 8, 20136 Milano, Italy; Centre for Applied Health and Social Care Research (CARe), Sheffield Hallam University, Sheffield, UK; SUPSI Scuola Universitaria per la Svizzera Italiana, Lugano, Switzerland.
Physiotherapy
|August 31, 2025
まとめ
非特異的な首の痛みに対する運動とマニュアルセラピーの試験の報告が不十分であるため,効果は過大評価される可能性があります. ランダム化制御試験 (RCT) の正確な結果のために,介入の精度に関するよりよいガイドラインが必要である.
科学分野:
- 理学療法
- 臨床試験の方法論
- 痛みの管理
背景:
- 非特異的な首痛 (NSNP) の治療には,しばしば運動とマニュアル療法が含まれます.
- ランダム化対照試験 (RCT) では,最適な介入マッチングのためのサブグループ識別が欠けている.
- 効果の異質性と介入の信頼性の変動は,不一致な発見に寄与する.
研究 の 目的:
- RCTにおける介入報告の質を評価し,NSNPに対するマッチングとマッチングのない介入を比較する.
- 介入報告の質と治療効果の見積もりとの関係を決定する.
主な方法:
- メタ解析による体系的なレビューの二次分析
- 介入報告の質はTIDieRチェックリストを用いて評価された.
- メタ・リグレッションでは,TIDieRスコアと痛みと障害に対する治療効果との関連を分析した.
主要な成果:
- 介入の正確性に関する報告は不十分であり,材料,提供者,場所,変更は不一致であった.
- 介入報告の質の低下は,対応した介入を好むより大きな治療効果の見積もりと相関しています.
- 短期的な痛みや障害の結果は,報告の質によって影響を受けた.
結論:
- RCTでの不十分な報告は,NSNPでのマッチング運動または手動療法による利益の過大評価につながる可能性があります.
- 介入の正確性に関する詳細が不十分であるため,研究設計,実施,および報告に関する改善されたガイドラインが必要である.
- NSNPの研究における信頼性の高い証拠のために,強化された報告基準は極めて重要です.
関連する概念動画
Statistical Significance
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
Multiple Comparison Tests
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Sign Test for Matched Pairs
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in value between...
To conduct the sign test, we first calculate the differences in value between...
Cochran's Q Test
Cochran's Q Test is a nonparametric statistical test used to determine if there are potential differences in the outcomes of three or more related groups on a binary (yes/no) or dichotomous outcome. It is essentially an extension of the McNemar Test, which is limited to two related samples - Cochran's Q test can handle three or more related samples, making it more versatile in scenarios where subjects are measured under multiple conditions. The test statistic follows a Chi-Square distribution,...
McNemar's Test
McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...

