Bibliographic record
Abstract
Osteoarthritis (OA) is the most common form of arthritis, affecting 1 in 3 people over age 65 and women more so than men. The prevalence of OA is rising due, in part, to the increasing prevalence of OA risk factors, including obesity, physical inactivity, and joint injury. OA-related joint pain causes functional limitations, poor sleep, fatigue, depressed mood and loss of independence. Compared to age and sex-matched peers, OA patients incur higher out of pocket health-related expenditures and substantial costs due to lost productivity. Most people with OA (59-87%) have at least one other chronic condition, especially cardiometabolic conditions. Symptomatic OA may impair the ability of people with cardiometabolic conditions to exercise and lose weight, resulting in increased risk for poor outcomes. People with OA and other chonic conditions are less likely to receive a diagnosis or recommended treatment. Further, in these individuals the most effective and safest treatment is physical activity/exercise coupled with self-management strategies, which is only moderately effective. Given the already high, and growing, burden of OA, enhanced effort is required to identify better - more effective and safe - treatments for the majority of people with OA who are living with other chronic conditions.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.016 |
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".