A multi-arm cluster randomized clinical trial of the use of knee kinesiography in the management of osteoarthritis patients in a primary care setting
Bibliographic record
Abstract
Objective: An important clinical gap reported by primary care physicians (PCPs) in managing knee osteoarthritis patients is the lack of validated tools to help them guide conservative treatment decision-making. This study aimed at evaluating the clinical utility of adding to current medical management (CMM) by PCPs, a dynamic knee kinesiography (KneeKG) exam assessing biomechanical risk factors linked to osteoarthritis progression.Design: In this 6-month cluster randomized controlled trial, primary care clinics were randomized into three groups: 1-CMM by PCPs, 2-CMM+KneeKG, and 3-CMM+KneeKG+Education (a self-management education session and two follow-up group meetings). Primary outcomes were scores on the Knee Injury and Osteoarthritis Outcome Score (KOOS) subscales and overall score.Results: Of the 894 patients referred from 87 clinics, 515 participated, 449 (87.2%) completed the study. At 6-month follow-up, patients in both KneeKG groups reported statistically significant improvement on the KOOS overall score (Group2: +5.5; Group3: +5.0), and on the symptoms, pain, and activities of daily living subscales compared to control group (all p < 0.05). They also reported significantly higher satisfaction levels with global care (both p < 0.01). Group 3-CMM+KneeKG+Education showed statistically significant improvements in objective functional tests as well as greater global impression of change in pain, function, quality of life, and global condition (all p < 0.05).Conclusions: Results demonstrated significant improvements in terms of pain, function, and satisfaction in KneeKG groups relative to the CMM. Adding education and supervision further improves clinical outcomes. These findings may support the added value of a KneeKG exam in assisting PCPs in the management of knee osteoarthritis patients.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".