Rethinking the role of cartilage loss: the influence of intra- and extra-articular factors on symptoms in advanced knee osteoarthritis
Bibliographic record
Abstract
Aims: Understanding the factors contributing to pain and function limitation in knee osteoarthritis (OA) is crucial to optimize the individual patient's management. This study aimed to quantify the role of cartilage degeneration, as well as other intra- and extra-articular factors, in determining clinical symptoms in patients with advanced knee OA. Methods: Subjects were selected from the Osteoarthritis Initiative database based on the criteria: Kellgren-Lawrence (KL) grades 3 to 4 and baseline clinical and MRI data. The analyzed data were: demographic parameters, KL grade, subchondral bone without cartilage coverage, anterior knee pain due to patellar quadriceps tendinitis, effusion, anserine bursa tenderness, meniscal extrusion, Hoffa's inflammation, bone marrow lesions (BMLs), visual analogue scale for pain, Western Ontario and McMaster Universities osteoarthritis index (WOMAC) total score, and WOMAC pain subscale. Results: The multivariate analysis on 233 knees demonstrated that VAS was influenced by the percentage of femoral subchondral bone without cartilage coverage (p < 0.001/η² = 0.058), patellar quadriceps tendinitis (p = 0.004/η² = 0.036), BMI (p = 0.013/η² = 0.027), age (p = 0.026/η² = 0.022), and anserine bursa tenderness (p = 0.033/η² = 0.020). However, the WOMAC total score was influenced by patellar quadriceps tendinitis (p < 0.001/η² = 0.114), BMI (p = 0.001/η² = 0.045), female sex (p = 0.016/η² = 0.025), and medial compartment BMLs (p = 0.015/η² = 0.029), but not by the extent of cartilage damage. Conclusion: The extent of cartilage degeneration influences the pain level, but is not the main factor driving the overall symptoms experienced in advanced knee OA. Other intra- and extra-articular factors, including patellar quadriceps tendinitis, anserine bursa tenderness, BMLs, and BMI have a greater impact on pain and functional impairment, and should be considered when choosing the most suitable treatment approach to manage knee OA patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 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".