Comparison of Structural Diagnosis and Management (SDM) approach and Myofascial Release (MFR) for improving planter heel pain, ankle range of motion and disability: A Randomized Clinical Trial
Bibliographic record
Abstract
Abstract Purpose This study compared the effect of Structural Diagnosis and Management (SDM) approach over Myofascial Release (MFR) on gastrocnemii, soleus and plantar fascia in patients with plantar heel pain. Subjects Sixty-four (n=64) subjects, aged 30-60 years, with a diagnosis of plantar heel pain, plantar fasciitis or calcaneal spur by a physician and according to ICD-10. Participants were equally allocated to MFR (n=32) and SDM (n=32) group by hospital randomization and concealed allocation. Methods In this assessor blinded randomized clinical trial, the control group performed MFR (three tissue specific stretching techniques) and the experimental group performed 2 tissue-specific interventions utilizing the Structural Diagnosis and Management (SDM) concept for 12 sessions over a 4-week period. In addition, both groups received strengthening exercises and other conventional treatments. Pain, activity limitations and disability were assessed as primary outcomes utilizing the foot function index (FFI) and range of motion (ROM) of the ankle dorsiflexors and plantar flexors were measured with a universal goniometer. Secondary outcomes were measured using the Foot Ankle Disability Index (FADI) and 10-point manual muscle testing process for the ankle dorsiflexors and plantar flexors. Result Both MFR and SDM groups exhibited significant improvements from baseline in all outcome variables, including: pain, activity level, disability, range of motion and function after the 12-week intervention period (p<.05), The SDM group showed more significant improvements than MFR for FFI pain (p=.001), FFI activity (p=.009), FFI (p= .001) and FADI (p=.002). Conclusion MFR and SDM approaches are both effective to reduce pain, improving function, ankle range of motion, and reduce disability in plantar heel pain. However, the SDM approach is significantly superior (for reducing pain, improving function and reducing disability (p<.05).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".