Wireless Motion Sensors—Useful in Assessing the Effectiveness of Physiotherapeutic Methods Used in Patients with Knee Osteoarthritis—Preliminary Report
Bibliographic record
Abstract
Osteoarthritis of the knee (OAK) is characterized by pain, limitation of joint mobility, and significant deterioration of proprioception resulting in functional decline. This study assessed proprioception in OAK patients following two ten-day rehabilitation programs using the Orthyo® system. Fifty-four study participants with clinical symptoms and radiological signs of OAK were randomly divided into an exercise group (n = 27) or a manual therapy group (n = 27). The control group consisted of 27 volunteers with radiological signs of OAK, but with no clinical symptoms or prior history of rehabilitation. The following parameters were assessed: knee proprioception using inertial sensors and a mobile application, patients’ function using Western Ontario and McMaster Universities osteoarthritis index (WOMAC), and pain intensity using the visual analog scale (VAS). Following rehabilitation, knee proprioception tests did not improve in either study group. Both study groups showed significant improvement of the WOMAC-assessed function (exercise group: p < 0.01, manual therapy group: p = 0.01) and a significant decrease (p < 0.01) of VAS-assessed pain following rehabilitation, but the post-therapy results did not differ significantly between the aforementioned groups. The Orthyo® system provided a quick and accurate assessment of the knee joint position sense. There was no direct relationship between functionality, pain, and proprioception threshold in the knee joint.
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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".