A pragmatic approach to prevent post-traumatic osteoarthritis after sport or exercise-related joint injury
Bibliographic record
Abstract
Lower extremity musculoskeletal injuries are common in sport and exercise, and associated with increased risk of obesity and post-traumatic osteoarthritis (PTOA). Unlike other forms of osteoarthritis, PTOA is common at a younger age and associated with more rapid progression, which may impact career choices, long-term general health and reduce quality of life. Individuals who suffer an activity-related joint injury and present with abnormal joint morphology, elevated adiposity, weak musculature, or become physically inactive are at increased risk of PTOA. Insufficient exercise therapy or incomplete rehabilitation, premature return-to-sport and re-injury, unrealistic expectations, or poor nutrition may further elevate this risk. Delay in surgical interventions in lieu of exercise therapy to optimize muscle strength and neuromuscular control while addressing fear of movement to guarantee resumption of physical activity, completeness of rehabilitation before return-to-sport, education that promotes realistic expectations and self-management, and nutritional counseling are the best approaches for delaying or preventing PTOA.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".