The relationship of three‐dimensional joint space width on weight‐bearing CT with pain and physical function
Bibliographic record
Abstract
Limitations of plain radiographs may contribute to poor sensitivity in the detection of knee osteoarthritis and poor correlation with pain and physical function. 3D joint space width, measured from weight bearing CT images, may yield a more accurate correlation with patients' symptoms. We assessed the cross-sectional association between 3D joint space width and self-reported pain and physical function. 528 knees (57% women) were analyzed from Multicenter Osteoarthritis Study participants. An upright weight bearing CT scanner was used to acquire bilateral, weight-bearing fixed-flexion images of the knees. A 3D dataset was reconstructed from cone beam projections and joint space width was calculated across the joint surface. The percentages of the apposed medial tibiofemoral joint surface with joint space width <2.0mm and <2.5mm respectively were calculated. Pain and physical function were measured using Western Ontario and McMaster Universities Osteoarthritis Index. Participants who reported greater pain severity tended to have a greater joint area with joint space width <2.0mm (p=.07 for the highest vs. the lowest tertile). Participants who reported greater functional limitations had a greater joint area with joint space width <2.0mm (p=.02 for the highest vs. the lowest tertile). There appears to be an association between the medial tibiofemoral area with joint space width <2.0mm and pain and physical function. This article is protected by copyright. All rights reserved.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".