Kellgren/Lawrence Grading in Cohort Studies: Methodological Update and Implications Illustrated Using Data From a Dutch Hip and Knee Cohort
Bibliographic record
Abstract
OBJECTIVE: The Cohort Hip and Cohort Knee (CHECK) is a cohort of middle-aged individuals with hip or knee pain. Radiographs were assigned Kellgren/Lawrence (K/L) scores under different conditions at each follow-up visit for 10 years. We aimed to describe and consolidate each scoring approach, then illustrate implications of their use by comparing baseline K/L scores assigned using 2 of these approaches, and evaluating their respective associations with joint replacement and incident radiographic osteoarthritis (ROA). METHODS: We compared baseline K/L scores assigned to hips and knees using 2 scoring approaches: 1) assigned by senior researchers to baseline images alone and 2) assigned by trained readers, with images read paired and in known sequence with up to 10 years of follow-up radiographs (Poisson regression). We evaluated the associations of baseline ROA (any: K/L grade ≥1; established: K/L ≥2) with joint replacement, and of K/L 1 joints with incident established ROA (survival analysis). RESULTS: ), the second scoring approach had 2.4 times (95% confidence interval [95% CI] 1.8-3.1 for knees) and 2.9 times (95% CI 2.3-3.7 for hips) higher prevalence of established ROA than the first approach. Established hip ROA had a higher risk of joint replacement using the first approach (hazard ratio [HR] 24.2 [95% CI 15.0-39.8] versus second approach HR 7.7 [95% CI 4.9-12.1]), as did knees (HR 19.3 [95% CI 10.3-36.1] versus second approach HR 4.8 [95% CI 2.4-9.6]). The risk of incident ROA did not differ by approach. CONCLUSION: This study demonstrates that evaluating ROA prevalence and predicting outcomes depends on the scoring approach.
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 | 0.278 | 0.326 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".