Issues in structure-modifying osteoarthritis drug development: new insights regarding radiographic clinical trial methods
Bibliographic record
Abstract
Over the past several decades, the osteoarthritis (OA) field has relied on radiographs in clinical trials, in order to assess longitudinal structural changes in weight-bearing joints such as the knee. Because alternative imaging modalities such as magnetic resonance imaging are beginning to mature, it is timely to compare and contrast the utility of these imaging methods for the purpose of conducting future structure-modifying OA drug clinical trials Insights from a large, multicenter knee OA study will be shared in the context of considering the next generation of longitudinal imaging methods to study OA. Patients in the study all had radiographic and symptomatic knee OA. A total of 2400 patients were randomized to the study, with an 85% study completion rate. The radiographic data for the study were collected at baseline, month 12 and month 24, using a highly standardized radiographic method directed at the medial compartment of the signal knee. Fluoroscopic confirmation of proper knee position was achieved at each study visit. A 2-year, 2400-patient study of knee OA has been completed and is being analyzed. Insights will be shared at the GARN Conference.
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 | 0.669 | 0.702 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".