Repeatability of objective bone and joint measures in the knee using weight-bearing computed tomography: three-dimensional quantification of bone mineral density, joint space width, and subchondral bone plate thickness
Bibliographic record
Abstract
Abstract Bone alterations and degenerative joint structural changes are frequently observed in knee osteoarthritis (OA). Objective measures of subchondral bone plate thickness (SBP.Th), apparent BMD, and joint space width (JSW) have begun to be used to better assess and understand disease progression. Weight-bearing CT (WBCT) allows 3D assessment of multiple bone and joint parameters; however, there is a paucity of literature investigating factors that might affect our ability to track changes over time. The purpose of this study was to investigate the repeatability of BMD, JSW, and SBP.Th measures obtained from WBCT images. Same-day, scan–rescan, knee WBCT images were acquired from 37 healthy adults (20 female, mean age: 24.6 yr). We quantified trabecular bone BMD at the proximal tibia and implemented joint space and cortical bone mapping to measure tibiofemoral JSW and SBP.Th, respectively. Test–retest repeatability was evaluated using coefficients of variation (CVRMS%) and least significant change (LSC). Mean trabecular BMD exhibited CVRMS% ranging from 1.87% to 2.85% and LSCs between 13.53 and 17.39 mgHA/cm3. For JSW, CVRMS% and LSC were less than 1% and 0.2 mm, respectively. SBP.Th measures had higher CVRMS% (9.75%-11.29%) but similar LSC values (0.09-0.26 mm) in comparison to JSW values. The high repeatability of subchondral bone and joint parameters underscore the potential of WBCT for quantifying and monitoring bone and joint changes in knee OA progression and management. However, standardization of acquisition and analysis methods will likely be required to ensure reliable evaluation over multiple time points.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".