Patient education for the management of subacromial pain syndrome: A scoping review
Bibliographic record
Abstract
OBJECTIVE: To identify the extent of the literature on patient education for subacromial pain syndrome (SAPS). METHODS: A scoping review was conducted in accordance with PRISMA-ScR standards. Nine databases were searched until November 2022 to identify articles describing patient education interventions for the management of SAPS. Interventions were extracted and described according to the Template for intervention description and replication (TIDieR) checklist and the core sets for shoulder-related health conditions of the International Classification of Functioning, Disability and Health (ICF). RESULTS: Sixty studies of various designs met the inclusion criteria, including thirty RCTs. Patient education was a primary intervention in seven of the included RCTs. In most of the educational interventions identified in the included studies, the descriptions did not adequately cover a majority of the TIDieR's checklist items. Patient education content was often mentioned and covered most, but not all, of the ICF core sets for shoulder disorders. CONCLUSION: Available data in current literature on patient education interventions for SAPS is scarce and lacks description. PRACTICE IMPLICATIONS: This study presents the content elements of patient education for the management of SAPS that are described in the literature and that clinicians could consider when treating individuals with SAPS.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".