Knee Osteoarthritis, Potential Mediators, and Risk of All‐Cause Mortality: Data From the Osteoarthritis Initiative
Bibliographic record
Abstract
OBJECTIVE: To assess the relation of symptomatic knee osteoarthritis (OA), knee pain, and radiographic knee OA to All-cause mortality and to identify mediators in the causal pathway. METHODS: Participants from the Osteoarthritis Initiative were divided into 4 groups: 1) symptomatic knee OA (i.e., both radiographic knee OA [Kellgren/Lawrence grade ≥2] and knee pain); 2) knee pain only; 3) radiographic knee OA only; and 4) neither radiographic knee OA nor knee pain. We examined the relation of knee OA status to All-cause mortality using a multivariable Cox proportional hazards model and assessed the extent to which the association was mediated by disability, physical component summary (PCS) and mental component summary (MCS) scores for quality of life (QoL), and use of oral pain-relief medications (i.e., nonsteroidal antiinflammatory drugs and opioids). RESULTS: Among 4,796 participants, 282 died over the 96-month follow-up period. Compared with those with neither radiographic knee OA nor knee pain, multivariable-adjusted hazard ratios (HRs) of mortality were 2.2 (95% confidence interval [95% CI] 1.6-3.1) for symptomatic knee OA, 0.9 (95% CI 0.6-1.4) for knee pain only, and 2.0 (95% CI 1.4-2.9) for radiographic knee OA only, respectively. Indirect effects (HRs) of symptomatic knee OA on mortality via disability and PCS of QoL were 1.1 (95% CI 1.0-1.4) and 1.2 (95% CI 1.0-1.4), respectively. No apparent mediation effect was observed through either MCS of QoL or oral pain-relief medications use. CONCLUSION: Participants with either symptomatic or radiographic knee OA were at an increased risk of All-cause mortality. Increased risk of mortality from symptomatic knee OA was partially mediated through its effect on disability and PCS of QoL.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".