Is soft tissue massage an effective treatment for mechanical shoulder pain? A study protocol
Bibliographic record
Abstract
Very little research has been conducted into the effectiveness of soft tissue massage as an intervention for the treatment of mechanical shoulder pain. Studies that have been conducted suffer from methodological issues, poor long-term follow-up and have conflicting results. The aim of this study, therefore, is to provide treating clinicians with improved evidence regarding the effectiveness of soft tissue massage for shoulder pain of local mechanical origin. Participants referred to the trial with mechanical shoulder pain will be assessed for range of motion, functional ability, and pain by a blinded assessor. Participants will then be randomly allocated to either an exercise-only group or an exercise and soft tissue massage group. Both groups will receive seven treatment sessions from a physical therapist over a period of 4 weeks. One week after the cessation of treatment, all participants will be reassessed by the same blinded assessor. Three months after cessation of treatment, subjects will again be reassessed. The primary outcome will be pain measured on a visual analogue scale (VAS) 1 week following the cessation of treatment. Secondary analyses will be pain at 3 months, the descriptive and present pain index sections of the short form McGill pain questionnaire, patient specific functional scale, and percentage improvement in pain scores and range of motion at 1 week following the cessation of treatment and at 3 month follow-up. Analysis of data will be carried out by a statistician who is blinded to group membership. Primary analyses will by intention-to-treat.
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 | 0.051 | 0.036 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.054 | 0.014 |
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".