Massage Therapy Effectiveness in Rehabilitation on Humeral Shaft Fracture in a Child: A Case Study
Bibliographic record
Abstract
Objectives: This case report aimed to explore the process and outcomes of a seven-week massage therapy treatment on post-surgical intervention to reduce humeral shaft fracture. Participant: An active 9-year-old girl who recently moved in the region and who underwent two surgeries following a humeral fracture with displacement after a fall at school. Intervention: The treatment used various techniques such as manual lymphatic drainage (MLD), myofascial release (MFR), therapeutic massage, and neuromuscular techniques (NMT) in conjunction with the physiotherapist rehabilitation programme to help the client recover both physically and emotionally from the trauma. Evaluation of the outcome measures (OM) took place throughout the study and after the four-week interim that followed the intervention period. Results: The massage therapy intervention indicated improvement regarding range of motion (ROM) and muscular strength. The clients' progress using the Patient-Specific Functional Scale (PSFS) indicated a gradual evolution to reach almost a 95% gain, and the Upper Extremity Function Index (UEFI) also showed improvement in everyday activities with a 21.5% positive change. The Child Outcome Rating Scale (CORS) and subsequent Child Session Rating Scale (CSRS) monitored therapeutic progress and indicated improvement on biopsychosocial (BPS) aspects throughout the treatment. Conclusion: The client felt strong and more confident after each massage intervention. A combination of techniques and the child's empowerment positively affected the client's overall wellness and confidence to return to activities.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".