Patient‐reported outcome measures in advanced musculoskeletal physiotherapy practice: a systematic review
Bibliographic record
Abstract
OBJECTIVE: Advanced practice physiotherapists (APPs), also known as extended scope physiotherapists, provide a new model of service delivery for musculoskeletal (MSK) disorders. Research to date has largely focused on health service efficiencies, with less emphasis on patient outcomes. The present systematic review aimed to identify the patient-reported outcome measures (PROMs) being utilized by APPs. METHOD: A wide search strategy was employed, including the PubMed, Embase, CINAHL, CENTRAL and PEDro databases, to identify studies relating to PROMs utilized by APPs in MSK healthcare settings. PROMs identified were classified into predetermined outcome domains, with additional contextual data extracted. RESULTS: Of the initial 12,302 studies, 38 met the inclusion criteria. These involved APPs across different settings, utilizing 72 different PROMs and most commonly capturing: Patient Satisfaction, Quality of Life (QoL), Functional Status, and Pain; and, less frequently: Global Status (i.e. overall improvement), Psychological Well-Being, Work ability, and Healthcare Consumption and Costs. The quality of the PROMs varied greatly, with Satisfaction most commonly measured utilizing non-standardized locally-devised tools; the EuroQol five-dimensions questionnaire (EuroQoL-5D) and 36-Item Short-Form (SF-36) cited most frequently to capture QoL; and the Visual Analogue Scale (VAS) to capture Pain. No key measure was identified to capture Functional Status, with 15 different tools utilized. CONCLUSION: APPs utilized a multiplicity of PROMs across a range of MSK disorders. The present review will act as an important resource, informing the selection of outcomes for MSK disorders, with a view to greater standardization of outcome measurement in MSK clinical practice, service evaluation and research.
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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 | 0.019 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".