Bibliographic record
Abstract
OBJECTIVE: The purpose of this review was to determine how effective manipulation and mobilization are in the treatment of chronic pain. METHODOLOGY: The literature search identified three systematic reviews addressing the effectiveness of manipulation and mobilization for low back pain, two systematic reviews addressing chronic neck pain, three randomized controlled trials addressing post-traumatic headache and neck pain, and one systematic review and one randomized controlled trial addressing upper limb (including shoulder) disorders. RESULTS: Most studies lacked details of the specific interventions, which were often combined with other interventions, and this could have enhanced or masked effectiveness. Subject groups were heterogeneous, and investigators did not indicate effectiveness for subgroups. Systematic reviews of chronic low back pain found evidence of effectiveness compared with placebo and with usual care. Evidence from the systematic reviews for chronic neck pain and from the systematic review and randomized controlled trial for chronic soft tissue shoulder disorders was contradictory. For post-traumatic headache, the randomized controlled trials reported a time-limited positive benefit or no different effects than comparison treatment. CONCLUSIONS: Manipulation and mobilization are more effective for chronic low back pain than placebos or usual care for up to 6 months (level 2). For chronic post-traumatic headache, evidence of effectiveness of manipulation and mobilization is limited (level 3). Manipulation and mobilization may or may not be effective for either chronic neck pain or chronic soft tissue shoulder disorders (level 4b).
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".