An evidence-informed rehabilitation management framework for posterior shoulder tightness: A scoping review
Bibliographic record
Abstract
Objective: To systematically scope the literature on posterior shoulder tightness (PST) and define a therapist-instructed and therapist-administered management framework. Design: Scoping review. Literature search: We searched MEDLINE, EMBASE, CINAHL, Scopus and Google Scholar from inception to December 2021. Study selection criteria: Peer-reviewed studies written in English, French, Greek, Japanese or Tamil, with extractable pre- and post-intervention data. Physiotherapy interventions amenable for posterior shoulder structural (muscle, capsule) causes of PST within an adult population. Data synthesis: Arksey and O'Malley's framework was implemented and the PRISMA extension for scoping reviews directed our data synthesis. The data charted from each study included authors, title, study year, location, study design; participant number, age, sex; PST intervention and parameters; patient-reported outcomes; and results. Themes were organized into therapist-instructed and therapist-administered rehabilitation strategies, as well as combined treatment methods. Results: Of 2777 articles identified from our search strategy, 21 articles were included. Therapist-instructed interventions included cross-body stretch (CBS), sleeper stretch (SS), a combination of the two and general stretching. Therapist-administered interventions included CBS, SS, instrument-assisted soft tissue mobilization (IASTM), muscle energy techniques, dry needling and Fauls protocol (12 therapist-assisted stretches). Combined interventions of tape with self-stretching and IASTM and stretching were also identified. Conclusion: Based on the current evidence, CBS and SS are the most researched treatments for PST and seem to be effective at improving PST. Furthermore, stabilization of the scapula while performing these stretches optimized the stretch targeted to the PST and ROM benefits for horizontal adduction.
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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 | 0.053 | 0.129 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.049 | 0.027 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".