Bibliographic record
Abstract
BACKGROUND: Multiple Sclerosis (MS) is characterized by degeneration of the myelin sheath of an axon resulting in decreased transmission of nerve impulses. It is an autoimmune disease with periods of exacerbation and remission. Types of MS include relapsing-remitting, acute progressive, chronic progressive attack-remitting, and benign. Symptoms vary from patient to patient. Common symptoms include fatigue, spasticity, swelling, and altered gait. MS is commonly treated with medications that help relieve symptoms and prolong disease progression. Massage Therapy (MT), specifically Swedish techniques, have been effective in treating MS. OBJECTIVE: To examine the effects of MT on mobility, fatigue, and edema in a patient with MS. METHODS: An MT student from MacEwan University's 2,200-hour Massage Therapy program administered five MT treatments over a six-week period to a 58-year-old female diagnosed with MS 11 years earlier. She presented with symptoms of decreased mobility, fatigue, and left ankle edema. Assessment included active and passive range of motion (ROM), myotomes, dermatomes, reflexes, and orthopedic tests. Goals for the treatment sessions were to increase mobility, decrease fatigue, and decrease edema. Assessment measures included the Timed-Up-and-Go (TUG) test for mobility, the Modified Fatigue Impact Scale (MFIS) to measure fatigue, and Figure-8 ankle measurement to measure edema. Techniques used included Swedish massage, passive ROM, manual lymphatic drainage (MLD), and home-care exercises. RESULTS: Little change was noted in mobility. The patient's fatigue level and left ankle edema decreased. CONCLUSION: The results suggest that MT is effective in reducing fatigue and edema in a patient with MS. Future studies are needed to evaluate the correlation between mobility and massage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 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".