Effectiveness of the McKenzie Method of Mechanical Diagnosis and Therapy for Treating Low Back Pain: Literature Review With Meta-analysis
Bibliographic record
Abstract
Study Design Literature review with meta-analysis. Background The McKenzie Method of Mechanical Diagnosis and Therapy (MDT), a classification-based system, was designed to classify patients into homogeneous subgroups to direct treatment. Objectives To examine the effectiveness of MDT for improving pain and disability in patients with either acute (less than 12 weeks in duration) or chronic (greater than 12 weeks in duration) low back pain (LBP). Methods Randomized controlled trials examining MDT in patients with LBP were identified from 6 databases. Independent investigators assessed the studies for exclusion, extracted data, and assessed risk of bias. The standardized mean difference (SMD) and 95% confidence interval were calculated to compare the effects of MDT to those of other interventions in patients with acute or chronic LBP. Results Of the 17 studies that met the inclusion criteria, 11 yielded valid data for analysis. In patients with acute LBP, there was no significant difference in pain resolution (P = .11) and disability (P = .61) between MDT and other interventions. In patients with chronic LBP, there was a significant difference in disability (SMD, -0.45), with results favoring MDT compared to exercise alone. There were no significant differences between MDT and manual therapy plus exercise (P>.05) for pain and disability outcomes. Conclusion There is moderate- to high-quality evidence that MDT is not superior to other rehabilitation interventions for reducing pain and disability in patients with acute LBP. In patients with chronic LBP, there is moderate- to high-quality evidence that MDT is superior to other rehabilitation interventions for reducing pain and disability; however, this depends on the type of intervention being compared to MDT. Level of Evidence Therapy, level 1a. J Orthop Sports Phys Ther 2018;48(6):476-490. Epub 30 Mar 2018. doi:10.2519/jospt.2018.7562.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.000 | 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".