Clinical effectiveness of the activator adjusting instrument in the management of musculoskeletal disorders: a systematic review of the literature.
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to conduct a systematic review of the literature investigating clinical outcomes involving the use of the Activator Adjusting Instrument (AAI) or Activator Methods Chiropractic Technique (AMCT). METHODS: A literature synthesis was performed on the available research and electronic databases, along with hand-searching of journals and reference tracking for any studies that investigated the AAI in terms of clinical effectiveness. Studies that met the inclusion criteria were evaluated using an instrument that assessed their methodological quality. RESULTS: Eight articles met the inclusion criteria. Overall, the AAI provided comparable clinically meaningful benefits to patients when compared to high-velocity, low-amplitude (HVLA) manual manipulation or trigger point therapy for patients with acute and chronic spinal pain, temporomandibular joint (TMJ) dysfunction and trigger points of the trapezius muscles. CONCLUSION: This systematic review of 8 clinical trials involving the use of the AAI found reported benefits to patients with a spinal pain and trigger points, although the clinical trials reviewed suffered from many methodological limitations, including small sample size, relatively brief follow-up period and lack of control or sham treatment groups.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".