Osteoarthritis Research Society International (OARSI): Past, present and future
Bibliographic record
Abstract
We provide a detailed account of the origin and establishment of the Osteoarthritis Research Society International (OARSI) and celebrate its history from inception to the current day. We discuss the mission, vision and strategic objectives of OARSI and how these have developed and evolved over the last 3 decades. We celebrate the achievements of the society as we approach its 30th birthday, honor the entire presidential line and respectfully pay tribute to the past presidents who are no longer with us. We reflect on the strong foundations of our society, OARSI's efforts to disseminate understanding of the health, disability and economic burdens of osteoarthritis (OA) to policymakers, and the exciting initiatives to make the society inclusive and international. We thank our corporate and industrial sponsors, who have supported us over many years, without whom our annual congresses would not have been possible. We celebrate our longstanding strategic partnership with our publisher, Elsevier, and the successful launch of our new journal Osteoarthritis and Cartilage Open, the most significant new development in our dissemination toolbox. For the first time in the history of the organization, our annual congress was cancelled in April 2020 and the 2021 meeting will be virtual. Despite the numerous challenges posed by the ongoing COVID-19 pandemic and the need to adapt quickly to a rapidly changing landscape, we must remain optimistic about the future. We will take advantage of new exciting opportunities to advance our mission and vision to enhance the quality of life of persons with 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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.015 |
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".