Osteopathic manipulative treatment in the management of headaches associated with musculoskeletal dysfunction: systematic review and meta-analysis
Bibliographic record
Abstract
CONTEXT: Osteopathic manipulative treatment (OMT) can be utilized as a primary or adjunctive treatment for headaches associated with musculoskeletal disorders. Although previous systematic reviews investigated the effectiveness of manual therapies for treating headaches, they did not focus specifically on OMT or perform a pooled meta-analysis to evaluate the effectiveness of outcomes. OBJECTIVES: The aim of this study is to systematically evaluate the effectiveness of OMT for managing headaches associated with musculoskeletal dysfunction and to assess the associated harm outcomes. METHODS: In September 2023, the following databases were searched for randomized controlled trials (RCTs) of adult patients with headaches associated with musculoskeletal dysfunction who were treated with OMT: Allied and Complementary Medicine Database, ClinicalTrials.gov, Cochrane Central Register of Controlled Trials (CENTRAL), Cumulative Index of Nursing and Allied Health Literature (CINAHL), Excerpta Medica (EMBASE), Osteopathic Medicine Digital Library (OSTMED), Ovid Emcare, Ovid MEDLINE, Physiotherapy Evidence Database (PEDro), PsycINFO, and PubMed. The search terms included osteopathic manipulative medicine, manual therapy, osteopath, headache, concussion, and head injury. The studies had to compare OMT techniques (e.g., articulatory [ART]; high-velocity, low-amplitude [HVLA]; soft tissues [ST]) to another form of treatment or a different type of OMT technique. Our primary outcomes included headache severity, headache frequency, disability associated with headaches, quality of life, and return to work (RTW); harm outcomes included all-cause dropout (ACD) rates, dropouts due to inefficacy, and adverse effects. The Cochrane Risk of Bias (ROB) tool was utilized to assess the ROB in the reviewed studies, and the quality of evidence was assessed utilizing the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system. Outcomes data were pooled for analysis and reported as standardized mean difference (SMD) and 95 % confidence interval (CI). RESULTS: Our search identified 11,657 RCTs. After removing duplicates and screening titles and abstracts, 167 underwent full-text review, and 18 were included in our review. None of the reviewed RCTs met all of the Cochrane ROB criteria. Moderate-quality evidence indicated that combined ART-HVLA (SMD=-0.61, 95 % CI=-1.0 to -0.23) and ST HVLA-ART (SMD=-0.48, 95 % CI=-0.83 to -0.13) effectively reduced the severity of headache. Moderate-quality evidence also indicated that the combined techniques of ART-HVLA (SMD=-0.43, 95 % CI=-0.74 to -0.13) and ST-ART-HVLA (SMD=-0.62, 95 % CI=-0.89 to -0.35) effectively reduced the frequency of headaches. Moderate-quality evidence indicated that quality of life was improved with combined ART-HVLA (SMD=0.57, 95 % CI=0.14 to 0.99). Low-quality evidence indicated no significant associations of OMT with disability or harm outcomes (all p>0.26). CONCLUSIONS: Results of our systematic review and meta-analysis suggested that a combination of multiple types of OMT techniques effectively reduced the frequency and severity of headaches and improved quality of life. However, high-quality RCTs with large sample sizes utilizing a variety of technique modalities and combinations of technique modalities are necessary to better evaluate the effectiveness of OMT for managing headaches.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".