Osteoarthritic pain: a review of current, theoretical and emerging therapeutics
Bibliographic record
Abstract
BACKGROUND: Despite exciting progress and growth in the understanding of molecular and cellular mechanisms of chronic pain, osteoarthritis (OA) pain remains a challenging clinical entity to treat. There is an emerging diversity of algogenic mechanisms suggesting heterogeneity in pain aetiology in the OA patient population. OBJECTIVE/METHODS: This review article summarises key issues in existing therapies for OA pain and highlights the emerging compounds in early and late development. It also highlights where tolerability may be a concern, especially in the older populations in which treatment interactions for co-morbid conditions may further narrow therapeutic index. Importantly, the authors also examine the diversity of biology that underpins OA pain and highlight the opportunities for the future. RESULTS/CONCLUSIONS: Many emerging therapies are presently in proof-of-concept clinical testing for treatment of OA. A growing understanding of the heterogeneity in the OA patient population, will challenge the ability to accurately understand observed efficacy or safety signals in these relatively small trials and how they may titrate to a broader patient population. We may need to wait several more years to understand whether the need for a differentiated therapy demanded by patients, payors and physicians alike, will be met.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".