Findings of extensive variation in the types of outcome measures used in hip and knee replacement clinical trials: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To describe the extent of variation in outcome measure usage in hip and knee replacement randomized trial literature, and to summarize this variation in the context of the International Classification of Functioning, Disability, and Health (ICF) conceptual model created by the World Health Organization (WHO). METHODS: We used a defined search strategy in Medline and EMBase databases to identify articles published from January 2000 to February 2007. Studies were reviewed if they were randomized trials with a >or=6-week followup and if they used noninvasive outcome measures of impaired joint function or whole-person limitations in daily activities or functional status. The WHO ICF model was used to categorize outcome measures. RESULTS: Of 972 studies, 160 were included for review. Of these, 82 were conducted on patients with hip replacements, 75 on patients with knee replacements, and 3 on patients with both. The most common outcome measure in knee trials was the American Knee Society score (used in 48% of reviewed studies), and in hip trials was the Harris hip score (52.4%). At least 20 different outcome measures were used in the hip trials, and at least 14 different measures were used in knee trials. The primary outcome was identified in only 24% of trials. CONCLUSION: We found extensive variation in outcome measures across trials and saw inconsistency across the components of the WHO ICF model. To improve interpretability, future work should determine whether consensus can be developed for a standardized set of outcome measures for hip and knee replacement trials.
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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 | 0.206 | 0.555 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".