Noisy knees - knee crepitus prevalence and association with structural pathology: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective: Knee crepitus, the audible crackling or grinding noise during knee movement, can be experienced across the lifespan and create concern for underlying pathology. Our systematic review aims to provide a summary estimate of knee crepitus prevalence and its association with structural pathology among the general population and across knee conditions. Design: Systematic review and meta-analysis. Data sources: Medline, Embase, CENTRAL, Web of Science, SPORTDiscus and CINAHL. Eligibility criteria: Studies evaluating knee crepitus prevalence. Results: 103 studies involving 36 439 participants (42 816 knees) were included. Based on very low certainty evidence, the pooled prevalence of knee crepitus in the general population was 41% (7609 knees; 95% CI 36% to 45%; I2=92.6%); in pain-free persons 36% (852 knees; 95% CI 23% to 50%; I2=91.9%), and in those with osteoarthritis (OA) 81% (18 821 knees; 95% CI 75% to 87%; I2=97.9%). Across other musculoskeletal knee conditions, the pooled prevalence of knee crepitus ranged from 35% (ligament injury; 2740 knees; 95% CI 27% to 44%; I2=95.6%) to 61% (cartilage pathology; 1445 knees; 95% CI 40% to 81%; I2=98.2%). There was low to very low certainty evidence of an association between knee crepitus and radiographic OA (OR 3.79, 95% CI 1.99 to 7.24; 1725 knees; I2=53.0%) and several OA-related features on magnetic resonance imaging (MRI). Conclusion: In this review, knee crepitus was prevalent in the general population, pain-free persons, those with knee OA and other musculoskeletal knee conditions. Knee crepitus was associated with a more than threefold increased odds of radiographic OA diagnosis and several OA-related MRI features. The low to very low certainty of evidence informing our aggregated prevalence estimates and association outcomes suggest that results should be interpreted with caution.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".