Monitoring van kniebelasting bij patiënten met artrose in de knie op basis van gegevens uit de praktijk - Inzichten uit biomechanische modellering met behulp van draagbare sensoren
Bibliographic record
Abstract
Osteoarthritis (OA) is the most common joint disease, that progressively undermines cartilage function. Knee OA Patients suffer from pain and impaired locomotor function. Patients minimize pain and optimize gait function through self-learned compensations. One of the first conservative treatment strategies in knee OA is intermittent pain medication, aiming to improve gait function by alleviating pain. To date no known cure or proven strategy exists for reducing progression from early to end-stage OA, thereby preventing the effective need for joint replacement prostheses. Altered joint loading - associated to obesity, malalignment, trauma or joint instability - is a critical risk factor for the onset and progression of OA. The impact of altered joint loadings in the movement patterns of the OA patients, knee joint loading should be accurately measured in clinical practise and real-life conditions. In the current project, we will first develop an innovative method based on wearable sensors, biomechanical modelling, to provide real-world data on knee joint loading during gait in patients with knee OA. Then, we will use the method to investigate how specific therapeutic interventions affect knee joint loading during gait in patients with knee OA. This project will allow to inform and help clinical practitioners with more complete joint loading measures and analysis that may on the long-term impact on the structural progression of the disease.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".