An international consensus on gaps in mechanisms of forced-based manipulation research: findings from a nominal group technique
Bibliographic record
Abstract
Force-Based Manipulation (FBM) including light touch, pressure, massage, mobilization, thrust manipulation, and needling techniques are utilized across several disciplines to provide clinical analgesia. These commonly used techniques demonstrate the ability to improve pain-related outcomes; however, mechanisms behind why analgesia occurs with these hands-on interventions has been understudied. Neurological, neuroimmune, biomechanical, neurovascular, neurotransmitter, and contextual factor interactions have been proposed to influence response; however, the specific relationships to clinical pain outcomes has not been well established. The purpose of this study was to identify gaps present within mechanism-based research as it relates to FBM. An international multidisciplinary nominal group technique (NGT) was performed and identified 37 proposed gaps across eight domains. Twenty-three of these gaps met consensus across domains supporting the complex multisystem mechanistic response to FBM. The strength of support for gaps within the biomechanical domain had less overall support than the others. Gaps assessing the influence of contextual factors had strong support as did those associating mechanisms with clinical outcomes (translational studies). The importance of literature investigating how FBM differs with individuals of different pain phenotypes (pain mechanism phenotypes and clinical phenotypes) was also presented aligning with other analgesic techniques trending toward patient-specific pain management (precision medicine) through the use of pain phenotyping.
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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 | 0.361 | 0.450 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".