Co-creation of an exercise inventory to improve scapular stabilization and control among individuals with rotator cuff-related shoulder pain: a survey-based study amongst physiotherapists
Bibliographic record
Abstract
BACKGROUND: Scapular stabilization exercises (SSE) are often included in the treatment of individuals with rotator cuff-related shoulder pain (RCRSP) to decrease pain and improve function. These SSE typically aim to strengthen the scapular muscles and optimize dynamic neuromuscular control of the scapula, which may improve overall shoulder stability and movement quality. No consensus of the recommended SSE for the management of RCRSP is available. Hence, this study aimed to consult physiotherapists to co-create an inventory of recommended SSE based on the exercise's relevance and frequency of prescriptions for the rehabilitation of individuals with RCRSP. METHODS: A group of 16 physiotherapists with experience in treating shoulder pain participated in a sequential consultation incorporating two distinct rounds of consultation focusing on SSE (modified Delphi design). In round 1, physiotherapists identified and demonstrated up to 10 SSE that they commonly recommend or use among individuals with RCRSP. The description and performance of all SSE were audio and video recorded. All SSE suggested by more than one participant in round 1 advanced to round 2. In round 2, physiotherapists rated these SSE on a 4-point Likert scale according to their perceived relevance and frequency of prescription for this population. RESULTS: In round 1, out of the 25 SSE recommended by participants, 19 SSE (76.0%) were recommended by more than one physiotherapist and advanced to round 2. In round 2, 13 SSE were consensually classified (agreement ≥75%) as being relevant for the rehabilitation of individuals with RCRSP. SSE targeting the recruitment of the serratus anterior and lower trapezius muscles were considered the most relevant for the management of RCRSP, whereas SSE targeting neuromuscular scapular control were the most prescribed SSE for the management of RCRSP. CONCLUSIONS: An inventory composed of 13 SSE was co-created by physiotherapists based on their relevance and frequency of prescription for the rehabilitation of individuals with RCRSP. When designing an exercise program, physiotherapists can use this SSE inventory to inform their exercise selection, in combination with their current knowledge on shoulder rehabilitation, as well as patients' preferences.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".