Effectiveness of Manual Therapy for Pain in Neck Pain Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Neck pain (NP) is a musculoskeletal public health problem that is often experienced, especially by modern society, with a high prevalence that can cause disability. Neck pain can be treated with various physiotherapy modalities, one of which is manual therapy/manipulation of the cervical spine. This study aims to determine the effectiveness of manual therapy in reducing pain in neck pain patients. Subjects and Method: This research is a systematic review and meta-analysis study, using PICO as follows, P= neck pain patients, I= manual therapy, C= other than manual therapy, O= reduction in pain. The articles included in this research were articles taken from several databases including Google Scholar, PubMed, NCBI, Science Direct, Embase, and Springer Link between 2010 and 2023. The keywords used to search for articles were: "neck pain" OR " chronic neck pain” OR “cervical pain” OR “cervicalgia” OR “upper cervical pain” OR “nonspecific neck pain” OR “nonspecific chronic neck pain” AND “manual therapy” OR “mobilization” OR “musculoskeletal manipulation” OR “cervical manipulation ” AND “RCT” OR “randomized controlled trial” OR “randomized controlled trial”. This research analysis was carried out using the RevMan 5.3 application. The results of the meta-analysis were reported using PRISMA flow diagrams. Results: A total of 9 articles have been analyzed originating from Spain, Canada, Germany, Turkey, and Pakistan. The study showed that patients with neck pain who received manual therapy experienced -2.01 units lower pain than those who did not receive manual therapy (SMD=-2.01; 95%CI=-3.00 to -1.03; p=0.001). Conclusion: Manual therapy significantly reduces neck pain compared to usual care.
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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 | 0.032 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| 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".