Quantitative Assessment of Knee Pain and Proprioception in Tibial Rotation in Individuals With Knee Osteoarthritis
Bibliographic record
Abstract
Patients with knee osteoarthritis (OA) exhibit reduced tibial rotations, possibly as a compensatory adaptation to mitigate mechanical stimuli on surrounding tissues. Hence, the passive threshold angle at which knee pain is sensed may reflect that surrounding tissues have received mechanical stimuli sufficient to exceed the nociceptive threshold. This study aimed to measure knee pain and proprioception during tibial rotation quantitatively. Twelve patients with symptomatic medial knee OA and 12 age- and sex-matched controls were included. For patients with knee OA, knee pain was quantified using the pain threshold angle (PTA), at which pain was first perceived during robot-controlled tibial internal rotation (IR) and external rotation (ER) at 0.5°/sec. Knee Injury and Osteoarthritis Outcome Score (KOOS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were also collected. Knee proprioception was measured as the threshold of passive movement detection (TPMD), at which participants first perceived the motion. Pearson's correlations were performed to assess relationships between KOOS/WOMAC and PTA. Independent t-tests compared TPMD between OA and control groups, while paired t-tests compared PTA and TPMD between IR and ER. In knee OA patients, smaller PTA in IR was correlated with worsened scores on KOOS/WOMAC (r = 0.59-0.70; p < 0.05); and PTA in IR was lower than in ER (p = 0.009), indicating increased pain sensitivity in IR. Patients with medial knee OA exhibited impaired proprioception, with a larger TPMD in tibial ER than controls (p = 0.019). This study presents a novel method for quantifying knee pain and proprioception, potentially enhancing the precision of knee OA rehabilitation.
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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 | 0.002 |
| 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) | 0.001 | 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".