Patella bone density is lower in knee osteoarthritis patients experiencing moderate-to-severe pain at rest.
Bibliographic record
Abstract
OBJECTIVE: To determine differences in patellar subchondral bone mineral density (BMD) between knee osteoarthritis (OA) patients with differing levels of pain at rest. METHODS: The preoperative knee of 41 total knee replacement (TKR) patients was scanned using QCT and scored for pain using Western Ontario McMasters Osteoarthritis Index (WOMAC). 'Pain at rest' was defined as average pain while lying//sitting and nocturnal pain. Participants were divided into groups: 'mild-to-no pain at rest' and 'moderate-to-severe pain at rest'. We used a depth-specific CT-based mapping technique to measure patellar subchondral BMD at depths of 0-2.5 mm, 2.5-5 mm, and 5-7.5 mm from the subchondral surface. Mean lateral and medial facet BMD were compared between groups using MANCOVA. RESULTS: Mean adjusted BMD was lower in participants with 'moderate-to-severe pain at rest' over the total lateral facet at depths of 0-2.5 mm (10% lower, p=0.041), 2.5-5 mm (20% lower, p=0.017), and 5-7.5 mm (25% lower, p=0.004), and over the total medial facet at 2.5-5 mm (22% lower, p=0.033) and 5-7.5 mm (28% lower, p=0.016). CONCLUSIONS: In OA patients with 'moderate-to-severe pain at rest', depth-specific density measures demonstrated up to 28% lower lateral and medial subchondral BMD. Patients with high levels of pain at rest may have reduced amounts of native bone prior to TKR.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".