OPTIKNEE 2022: consensus recommendations to optimise knee health after traumatic knee injury to prevent osteoarthritis
Bibliographic record
Abstract
The goal of the OPTIKNEE consensus is to improve knee and overall health, to prevent osteoarthritis (OA) after a traumatic knee injury. The consensus followed a seven-step hybrid process. Expert groups conducted 7 systematic reviews to synthesise the current evidence and inform recommendations on the burden of knee injuries; risk factors for post-traumatic knee OA; rehabilitation to prevent post-traumatic knee OA; and patient-reported outcomes, muscle function and functional performance tests to monitor people at risk of post-traumatic knee OA. Draft consensus definitions, and clinical and research recommendations were generated, iteratively refined, and discussed at 6, tri-weekly, 2-hour videoconferencing meetings. After each meeting, items were finalised before the expert group (n=36) rated the level of appropriateness for each using a 9-point Likert scale, and recorded dissenting viewpoints through an anonymous online survey. Seven definitions, and 8 clinical recommendations (who to target, what to target and when, rehabilitation approach and interventions, what outcomes to monitor and how) and 6 research recommendations (research priorities, study design considerations, what outcomes to monitor and how) were voted on. All definitions and recommendations were rated appropriate (median appropriateness scores of 7-9) except for two subcomponents of one clinical recommendation, which were rated uncertain (median appropriateness score of 4.5-5.5). Varying levels of evidence supported each recommendation. Clinicians, patients, researchers and other stakeholders may use the definitions and recommendations to advocate for, guide, develop, test and implement person-centred evidence-based rehabilitation programmes following traumatic knee injury, and facilitate data synthesis to reduce the burden of knee post-traumatic knee OA.
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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 | 0.121 | 0.238 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.018 |
| Bibliometrics | 0.016 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.011 | 0.013 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.023 | 0.012 |
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".