Psychometric properties of the Musculoskeletal Function Assessment and the Short Musculoskeletal Function Assessment: a systematic review
Bibliographic record
Abstract
OBJECTIVES: To investigate the psychometric properties of the Musculoskeletal Function Assessment (MFA) and Short Musculoskeletal Function Assessment (SMFA). DATA SOURCES: A systematic search of the following databases was undertaken concerning psychometric evidence of the MFA and SMFA: PubMed, Embase, Scopus and Cinahl. References of retrieved articles were inspected for additional data. REVIEW METHOD: Articles evaluating the validity, reliability or responsiveness of the MFA or SMFA in patients with musculoskeletal disorders were included in this systematic review. The methodological quality of included articles was critically appraised and the psychometric data were extracted using standardized forms. An established set of criteria were used to synthetize the evidence in order to highlight the strengths and weaknesses of included questionnaires and the gaps in the literature. RESULTS: Nine articles on MFA and 24 articles on SMFA met the inclusion criteria. The SMFA fulfilled 75% of the psychometric criteria analyzed, while the MFA fulfilled only 50%. MFA and SMFA have excellent content validity and relative reliability (weighted average intraclass correlation coefficient ⩾ 0.87), and are moderately to highly responsive (standardized response mean between 0.65 and 1.13). Absolute reliability and clinically important difference of both questionnaires need to be defined, while the construct validity of MFA still needs to be established. CONCLUSION: MFA and SMFA are reliable and responsive tools for monitoring the function of patients with various musculoskeletal disorders. Still, research is needed to justify their usage in a clinical setting.
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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 | 0.045 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".