The bulge sign – a simple physical examination for identifying progressive knee osteoarthritis: data from the Osteoarthritis Initiative
Bibliographic record
Abstract
OBJECTIVE: To examine whether the presence of bulge sign or patellar tap was associated with frequent knee pain, progression of radiographic OA (ROA) and total knee replacement (TKR). METHODS: This study included 4344 Osteoarthritis Initiative participants examined at baseline for bulge sign and/or patellar tap. The clinical signs were categorized as no (none at baseline and 2 years), resolved (present at baseline only), developed (present at 2 years only) and persistent (present at both time points). Frequent knee pain and progression of ROA over 4 years and TKR over 6 years were assessed. Binary logistic regression was used to examine the associations. RESULTS: A total of 12.7% of participants had bulge sign only, 2.0% had patellar tap only and 3.3% had both. A positive baseline bulge sign was associated with an increased risk of frequent knee pain [OR 1.31 (95% CI 1.04, 1.64), P = 0.02] and TKR [OR 1.47 (95% CI 1.06, 2.05), P = 0.02]. Developed bulge sign was associated with an increased risk of frequent knee pain [OR 1.75 (95% CI 1.34, 2.29), P < 0.001] and progressive ROA [OR 1.67 (95% CI 1.11, 2.51), P = 0.01]. Persistent bulge sign was associated with an increased risk of frequent knee pain [OR 1.60 (95% CI 1.09, 2.35), P = 0.02], progressive ROA [OR 1.84 (95% CI 1.01, 3.33), P = 0.045] and TKR [OR 2.13 (95% CI 1.23, 3.68), P = 0.007]. Patellar tap was not examined for its association with joint outcomes due to its low prevalence. CONCLUSION: The presence of bulge sign identifies individuals at increased risk of frequent knee pain, progression of ROA and TKR. This provides clinicians with a quick, simple, inexpensive method for identifying those at higher risk of progressive knee OA who should be targeted for therapy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".