Toward designing human intervention studies to prevent osteoarthritis after knee injury: A report from an interdisciplinary OARSI 2023 workshop
Bibliographic record
Abstract
Objective: The global impact of osteoarthritis is growing. Currently no disease modifying osteoarthritis drugs/therapies exist, increasing the need for preventative strategies. Knee injuries have a high prevalence, distinct onset, and strong independent association with post-traumatic osteoarthritis (PTOA). Numerous groups are embarking upon research that will culminate in clinical trials to assess the effect of interventions to prevent knee PTOA despite challenges and lack of consensus about trial design in this population. Our objectives were to improve awareness of knee PTOA prevention trial design and discuss state-of-the art methods to address the unique opportunities and challenges of these studies. Design: An international interdisciplinary group developed a workshop, hosted at the 2023 Osteoarthritis Research Society International Congress. Here we summarize the workshop content and outputs, with the goal of moving the field of PTOA prevention trial design forward. Results: Workshop highlights included discussions about target population (considering risk, homogeneity, and possibility of modifying osteoarthritis outcome); target treatment (considering delivery, timing, feasibility and effectiveness); comparators (usual care, placebo), and primary symptomatic outcomes considering surrogates and the importance of knee function and symptoms other than pain to this population. Conclusions: Opportunities to test multimodal PTOA prevention interventions across preclinical models and clinical trials exist. As improving symptomatic outcomes aligns with patient and regulator priorities, co-primary symptomatic (single or aggregate/multidimensional outcome considering function and symptoms beyond pain) and structural/physiological outcomes may be appropriate for these trials. To ensure PTOA prevention trials are relevant and acceptable to all stakeholders, future research should address critical knowledge gaps and challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.396 | 0.214 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.022 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".