Do Knee Moments Normalized to Measures of Knee Cartilage Area Better Classify the Severity of Knee Osteoarthritis?
Bibliographic record
Abstract
Investigations of joint loading in knee osteoarthritis (OA) typically normalize the knee adduction moment to global measures of body size (eg, body mass, height) to allow comparison between individuals. However, such measurements may not reflect knee size. This study used a morphometric measurement of the cartilage surface area on the medial tibial plateau, which better represents medial knee size. This study aimed to determine whether normalizing the peak knee adduction moment and knee adduction moment impulse during gait to the medial tibial bone-cartilage interface could classify radiographic knee OA severity more accurately than traditional normalization techniques. Individuals with mild (N = 22) and severe (N = 17) radiographic knee OA participated. The medial tibial bone-cartilage interface was quantified from magnetic resonance imaging scans. Gait analysis was performed, and the peak knee adduction moment and knee adduction moment impulse were calculated in nonnormalized units and normalized to body mass, body weight × height, and the medial tibial bone-cartilage interface. Receiver operating characteristic curves compared the ability of each knee adduction moment normalization technique to classify participants according to radiographic disease severity. No normalization technique was superior at distinguishing between OA severities. Knee adduction moments normalized to medial knee size were not more sensitive to OA severity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".