The effectiveness of spinal manipulative therapy procedures for spine pain: protocol for a systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Spinal manipulative therapy (SMT) is a guideline-recommended treatment option for spinal pain. The recommendation is based on multiple systematic reviews. However, these reviews fail to consider that clinical effects may depend on SMT "application procedures" (i.e., how and where SMT is applied). Using network meta-analyses, we aim to investigate which SMT "application procedures" have the greatest magnitude of clinical effectiveness for reducing pain and disability, for any spinal complaint, at short-term and long-term follow-up. We will compare application procedural parameters by classifying the thrust application technique and the application site (patient positioning, assisted, vertebral target, region target, Technique name, forces, and vectors, application site selection approach and rationale) against: 1. Waiting list/no treatment; 2. Sham interventions not resembling SMT (e.g., detuned ultrasound); 3. Sham interventions resembling SMT; 4. Other therapies not recommended in clinical practice guidelines; and 5. Other therapies recommended in clinical practice guidelines. Secondly, we will examine how contextual elements, including procedural fidelity (whether the SMT was delivered as planned) and clinical applicability (whether the SMT is similar to clinical practice) of the SMT. METHODS: We will include randomized controlled trials (RCT) found through three search strategies, (i) exploratory, (ii) systematic, and (iii) other known sources. We define SMT as a high-velocity low-amplitude thrust or grade V mobilization. Eligibility is any RCT assessing SMT against any other type of SMT, any other active or sham intervention, or no treatment control on adult patients with pain in any spinal region. The RCTs must report on continuous pain intensity and/or disability outcomes. Two authors will independently review title and abstract screening, full-text screening, and data extraction. Spinal manipulative therapy techniques will be classified according to the technique application and choice of application sites. We will conduct a network-meta analysis using a frequentist approach and multiple subgroup and sensitivity analyses. DISCUSSION: This will be the most extensive review of thrust SMT to date, and will allow us to estimate the importance of different SMT application procedures used in clinical practice and taught across educational settings. Thus, the results are applicable to clinical practice, educational settings, and research studies. PROSPERO registration: CRD42022375836.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| 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".