Reliability and validity of manual palpation for the assessment of patients with low back pain: a systematic and critical review
Bibliographic record
Abstract
BACKGROUND: Static or motion manual palpation of the low back is commonly used to assess pain location and reproduction in low back pain (LBP) patients. The purpose of this study is to review the reliability and validity of manual palpation used for the assessment of LBP in adults. METHOD: We systematically searched five databases from 2000 to 2019. We critically appraised internal validity of studies using QAREL and QUADAS-2 instruments. We stratified results using best-evidence synthesis. Validity studies were classified according to Sackett and Haynes. RESULTS: We identified 2023 eligible articles, of which 14 were low risk of bias. Evidence suggests that reliability of soft tissue structures palpation is inconsistent, and reliability of bony structures and joint mobility palpation is poor. We found preliminary evidence that gluteal muscle palpation for tenderness may be valid in differentiating LBP patients with and without radiculopathy. CONCLUSION: Reliability of manual palpation tests in the assessment of LBP patients varies greatly. This is problematic because these tests are commonly used by manual therapists and clinicians. Little is known about the validity of these tests; therefore, their clinical utility is uncertain. High quality validity studies are needed to inform the clinical use of manual palpation tests.
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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 | 0.021 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".