Bridging Disciplines as a pathway to Finding New Solutions for Osteoarthritis a collaborative program presented at the 2019 Orthopaedic Research Society and the Osteoarthritis Research Society International
Bibliographic record
Abstract
Objective: To stimulate future research directions that seek solutions for osteoarthritis (OA) at the interface between diverse disciplines and address osteoarthritis (OA) as a serious disease with a complexity that has presented a barrier to finding safe effective solutions. Methods: Sessions were conducted at the 2019 meetings of the Orthopaedic Research Society (ORS) and Osteoarthritis Research Society International (OARSI) that included presentations and questions/comments submitted from leading OA researchers representing imaging, mechanics, biomarkers, phenotyping, clinical, epidemiology, inflammation and exercise. Results: Solutions for OA require a paradigm shift in research and clinical methods in which OA is contextualized as a complex whole-body/person disease. New OA definition(s)/phenotype(s) and OA markers/signals are needed to address the interplay between genetic and environmental factors of the disease as well as capture the mechanosensitivity of the disease. The term "Mechanokines" was proposed to highlight the importance of incorporating whole body mechanics as a marker of early OA. New interventions and apparent paradoxical observations/questions (e.g. exercise vs. load modification) were also discussed in the context of considering OA as a complex system. Conclusion: To advance new OA treatments that are safe and effective, OA should be considered as a "Whole Person" disease. This approach requires a concerted effort to bridge disciplines and include interactions across scales from the molecule to the whole body, including psychosocial aspects.
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 | 0.041 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".