Structural Integration Case Report: a Global Intervention Challenging the Limitations of Local Rehabilitation
Bibliographic record
Abstract
BACKGROUND: Conventional rehabilitation for musculoskeletal injuries post-surgery is generally site-specific and aims to return the person to 'normal' function. Commonly, conventional treatment focuses locally and little or no attention is given to comorbidities, other symptoms, postural compensations, or adaptations either pre-existing or resulting from the injury. Structural Integration (SI) is a manual therapy applied to and focusing on fascial continuities throughout the whole body. This case report explores SI as a global, whole-body intervention for rehabilitation. PURPOSE: To examine the effects of a whole-body approach that addresses local and global symptoms following ankle surgery. METHODS: The Anatomy Trains Structural Integration (ATSI formerly KMI) 12-series protocol was applied and a selection of outcome measures were used to track progress and assess the efficacy of SI. Ankle mobility and function was assessed primarily using Weight-Bearing Lunge Test and Lower Extremity Functional Scale. Local pain was reported using the McGill Pain Questionnaire. General well-being was evaluated using subjective questioning and the WHO Quality of Life Questionnaire. RESULTS: Local results included increased mobility and function to affected leg, and reduced pain and swelling. Global results included an improvement in physical and psychological well-being, with the reduction of pain and dysfunction in other areas. CONCLUSION: This case report demonstrates global benefits of a whole-body approach when structural integration is applied during rehabilitation. More clinical research that includes SI is needed to determine if the local and global results shown in this case study can be demonstrated in additional rehabilitation populations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".