Toward a Clinical Definition of Early Osteoarthritis: Onset of Patient‐Reported Knee Pain Begins on Stairs. Data From the Osteoarthritis Initiative
Bibliographic record
Abstract
OBJECTIVE: Early detection of osteoarthritis (OA) would increase the chances of effective intervention. We aimed to investigate which patient-reported activity is first associated with knee pain. We hypothesized that pain would occur first during activities requiring weight bearing and knee bending. METHODS: Data were obtained from the Osteoarthritis Initiative (OAI), a multicenter, longitudinal prospective observational cohort of people who have or are at high risk of OA. Participants completed the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC; Likert scale) annually for up to 7 years. Rasch analysis was used to rank the WOMAC pain questions (activities) in order of affirmation as the pain score increased from 0. For each total WOMAC score category (0-20) we selected 25 individuals at random based on their maximum score across all visits. Fit to the Rasch model was assessed in this subset; stability of question ranking over successive visits was confirmed in the full OAI. RESULTS: WOMAC data on 4,673 people were included, with 491 selected for subset analysis. The subset data showed good fit to the Rasch model (χ(2) = 43.31, P = 0.332). In the full OAI, the "using stairs" question was the first to score points as the total pain score increased from 0 (baseline logit score ± 95% confidence interval -4.74 ± 0.07), then "walking" (-2.94 ± 0.07), "standing" (-2.65 ± 0.07), "lying/sitting" (-2.00 ± 0.08), and finally "in bed" (-1.32 ± 0.09). This ordering was consistent over successive visits. CONCLUSION: Knee pain is most likely to first appear during weight-bearing activities involving bending of the knee, such as using stairs. First appearance of this symptom may identify a group suitable for early intervention strategies.
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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".