Measures for Pain and Function Assessments for Patients With Osteoarthritis
Bibliographic record
Abstract
INTRODUCTION: To develop an osteoarthritis (OA) quality measure that satisfies quality-reporting initiatives, a systematic review of the literature was undertaken to identify and evaluate measures of pain and function commonly used to assess outcomes in patients with upper and lower extremity OA. METHODS: English-language systematic reviews and meta-analyses evaluating validity of pain and function instruments in OA patients published between 1995 and 2014 were considered for inclusion. The quality of all included studies was assessed using the Appraisal of Guidelines for Research and Evaluation II instrument (AGREE II). RESULTS: More than 90 pain and/or functional assessment tools were evaluated within the 16 systematic reviews included in this analysis. Of the 16 systematic reviews, 6 articles had high-quality study designs; the remaining 10 reviews had moderate-quality study designs. CONCLUSION: There currently exists no OA pain and functional assessment tool capable of meeting the stringent requirements established by newer quality-reporting programs. The use of invalidated or unreliable patient-reported outcome measures may improperly estimate patient pain and functional status, which could affect treatment options, patient satisfaction, reimbursement, and/or quality of life. LEVEL OF EVIDENCE: II.
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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 | 0.028 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".