Knee Osteoarthritis Worsening Across the Disease Spectrum and Future Knee Pain, Symptoms, and Functioning: A Multisite Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Prognostic estimates for knee osteoarthritis worsening over the full disease spectrum have not been reported. Osteoarthritis Initiative data were used to determine the association between knee radiographic osteoarthritis worsening and future knee pain and function. METHODS: Yearly data over a 5-year period were analyzed. Outcome measures were the Western Ontario and McMaster Universities Osteoarthritis Index and the Knee Injury and Osteoarthritis Outcome Score pain and symptoms scales. Knees were grouped based on whether yearly Kellgren/Lawrence (K/L) grades worsened. Associations of yearly K/L worsening with knee pain, symptoms, or function scores at the end of the year and with lagged outcomes observed at least 1 year after worsening were assessed using linear models with generalized estimating equations. RESULTS: A total of 25,932 knee-years of observations were examined in the primary analysis. For knees with yearly K/L worsening, all outcome scores were significantly worse (P values from 0.02 to <0.001) at the end of the index year, as compared to unchanged knees. All outcomes for knees with worsened versus unchanged K/L scores exceeded the minimal clinically important difference (MCID). For lagged effects, outcomes at 1 year following the index K/L year exceeded the MCID and were statistically significant (P ≤ 0.001) for knees with baseline K/L grades of 2 or 3. CONCLUSION: K/L worsening over a 1-year period is prognostically important over the short term for all baseline K/L grades, and for knees with baseline K/L grades of 2 or 3 worsening continues over the following year.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".