It Is the Time to Think About a Treat-to-Target Strategy for Knee Osteoarthritis
Bibliographic record
Abstract
A Migliore,1 G Gigliucci,1 RJ Petrella,2 RR Bannuru,3 X Chevalier,4 E Maheu,5 R Raman,6 G Herrero-Beaumont,7 N Isailovic,8 M Matucci Cerinc9 1Rheumatology Unit, San Pietro Fatebenefratelli Hospital, Rome, Italy; 2Department of Family Medicine, School of Kinesiology Western University, Western Centre for Public Health & Family, London, Ontario, Canada; 3Center for Treatment Comparison and Integrative Analysis Division of Rheumatology, Tufts Medical Center, Boston, MA, USA; 4Department of Rheumatology, Hôpital Henri Mondor, Creteil, France; 5Rheumatology Department, AP-HP, Saint-Antoine Hospital, Paris, France; 6Academic Department of Orthopaedics, Hull and East Yorkshire NHS Trust, Castle Hill Hospital, Cottingham, UK; 7Joint and Bone Research Unit, IIS-Fundacion Jimenez Diaz UAM, Madrid, Spain; 8Division of Rheumatology and Clinical Immunology, Humanitas Research Hospital, Rozzano, Milan, Italy; 9Division of Rheumatology AOUC, Department of Experimental and Clinical Medicine, University of Florence, Florence, ItalyCorrespondence: N IsailovicDivision of Rheumatology and Clinical Immunology, Humanitas Research Hospital, Via A. Manzoni 56, Rozzano 20089, Milan, ItalyTel +39-02-8224-5118Email natasa.isailovic@humanitasresearch.itAbstract: Osteoarthritis (OA) is a rheumatic disease that affects the well-being of the patient, compromises physical and mental function, and affects other quality of life aspects. In the literature, several evidence-based guidelines and recommendations for the management of knee osteoarthritis (KOA) are available. These recommendations list the different therapeutic options rather than addressing a hierarchy between the treatments and defining the real target. Therefore, a question arises: are patients and physicians satisfied with the current management of KOA? Actually, the answer may be negative, thus suggesting a change in our therapeutic strategies. In this article, we address this challenge by suggesting that it is time to develop a “treat to target strategy” for KOA.Keywords: osteoarthritis, knee osteoarthritis, treat to target
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.021 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".