Factors Associated With Clinical Responses to Spinal Manipulation in Patients With Non-specific Thoracic Back Pain: A Prospective Cohort Study
Bibliographic record
Abstract
Introduction: The management of musculoskeletal disorders is complex and requires a multidisciplinary approach. Manual therapies, such as spinal manipulative therapy (SMT), are often recommended as an adjunct treatment and appear to have demonstrable effects on pain and short-term disability in several spinal conditions. However, no definitive mechanism that can explain these effects has been identified. Identifying relevant prognostic factors is therefore recommended for people with back pain. Objective: The main purpose of this study was to identify short-term candidate prognostic factors for clinically significant responses in pain, disability and global perceived change (GPC) following a spinal manipulation treatment in patients with non-specific thoracic back pain. Methods: Patients seeking care for thoracic spine pain were invited to participate in the study. Pain levels were recorded at baseline, post-intervention, and 1 week after a single session of SMT. Disability levels were collected at baseline and at 1-week follow-up. GPC was collected post-intervention and at 1-week follow-up. Biomechanical parameters of SMT, expectations for improvement in pain and disability, kinesiophobia, anxiety levels as well as perceived comfort of spinal manipulative therapy were assessed. Analysis: Differences in baseline characteristics were compared between patients categorized as responders or non-responders based on their pain level, disability level, and GPC at each measurement time point. Binary logistic regression was calculated if the statistical significance level of group comparisons (responder vs. non-responders) was equal to, or <0.2 for candidate prognostic factors. Results: 107 patients (62 females and 45 males) were recruited. Mean peak force averaged 450.8 N with a mean thrust duration of 134.9 ms. Post-intervention, comfort was associated with pain responder status ( p < 0.05) and GPC responder status ( p < 0.05), while expectation of disability improvement was associated with GPC responder status ( p < 0.05). At follow-up, comfort and expectation of pain improvement were associated with responder GPC status ( p < 0.05). No association was found between responder pain, disability or GPC status and biomechanical parameters of SMT at any time point. Discussion: No specific dosage of SMT was associated with short-term clinical responses to treatment. However, expectations of improvement and patient comfort during SMT were associated with a positive response to treatment.
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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 | 0.029 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".