Comparing participant recruitment methods in knee osteoarthritis: Implications for community recruitment and its effects on clinical and biomechanical outcomes
Bibliographic record
Abstract
BACKGROUND: Community-based recruitment may relieve clinician-dependant strategies for participant recruitment in gait analyses for clinical populations. However, it is unknown whether individuals recruited through community-based self-report methods exhibit similar patient-reported outcomes and gait biomechanics to those clinically diagnosed by a healthcare provider. This study aims to explore the differences between self-reported and clinically diagnosed knee osteoarthritis in terms of pain, function, quality of life, and gait biomechanics. METHODS: Participants with self-reported knee osteoarthritis (n = 16) were recruited based on activity-related knee pain, while those with clinically diagnosed knee osteoarthritis (n = 16) diagnosed based on the American College of Rheumatology guidelines by an orthopaedic surgeon. Both groups completed the Knee Injury and Osteoarthritis Outcome Score and Intermittent and Constant Osteoarthritis Pain questionnaires. Gait analysis was performed using three-dimensional motion capture, with sagittal plane knee angles, and knee flexion and adduction moments. Independent t-tests and statistical parametric mapping were used for group comparisons. FINDINGS: No differences were found between groups for patient-reported outcomes. Compared to individuals with clinically diagnosed knee osteoarthritis, individuals with self-reported knee osteoarthritis walked with reduced knee flexion angles and reduced peak knee flexion and adduction moments. INTERPRETATION: While patient-reported outcomes were not different, the biomechanical characteristics indicate that individuals recruited using community-based self-reported methodology may walk with gait patterns more closely resembling severe knee osteoarthritis. Although neither recruitment strategy is superior, these data support that employing a community-based self-report recruitment criterion may yield individuals who walk with gait patterns more closely resembling severe knee osteoarthritis compared to those clinically diagnosed.
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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 | 0.232 | 0.370 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".