Systematic review of measurement properties of patient-reported outcome measures in patients with elbow-related orthopedic conditions
Bibliographic record
Abstract
Background: Elbow-related issues are common among adults and can significantly impact their daily activities and quality of life. Patient-reported outcome measures (PROMs) are increasingly being used nowadays to assess the subjective experience of patients with elbow problems. Identifying reliable, valid, and responsive tools is deemed necessary to be embedded in the assessment and treatment planning. This study aimed to identify currently available PROMs designed for people with elbow pathology and appraise, compare, and synthesize psychometric data supporting these PROMs. Methods: This review adhered to the methodology outlined by Cochrane and followed the principles set forth by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Systematic searches were conducted in databases such as MEDLINE and Embase to identify studies related to PROMs for elbow diseases. The search was limited to papers published until July 2023. The inclusion criteria were specifically targeted toward adult populations, English-language publications, and research that provided information on the psychometric features of PROMs. The COSMIN Risk of Bias checklist was used to do data extraction and quality assessment. Results: Of the 6741 articles, 58 full-text articles were evaluated to determine their eligibility. Nineteen studies fulfilled the requirements for inclusion, providing information on 10 PROMs for elbow problems. PROMs such as the Disabilities of the Arm, Shoulder, and Hand, Quick Disabilities of Arm, Shoulder, and Hand, and Oxford Elbow Score have shown strong reliability and construct validity. Most PROMs did not have thorough qualitative evaluations and concept elicitation. The Patient-Rated Ulnar Nerve Evaluation and Patient-Rated Elbow Evaluation showed strong reliability and responsiveness, but the Elbow Self-Assessment Score and Kerlan-Jobe Orthopaedic Clinic score exhibited satisfactory reliability but lacked comprehensive qualitative assessment. Conclusions: Ten elbow PROMs were reviewed, showing psychometric diversity. Some PROMs are reliable and valid; others require further development and validation. Future studies could enhance qualitative assessments and standardization of PROM evaluation guidelines. A core outcome set for elbow-related PROMs could improve consistency and comparability in clinical 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 | 0.038 | 0.196 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".