Bibliographic record
Abstract
PURPOSE OF REVIEW: This review examines recent advances in understanding the abnormal mechanics characteristic of knee osteoarthritis and provides a rationale to assess the total exposure to knee loading during daily activity. RECENT FINDINGS: The abnormal loading environment in knee osteoarthritis is represented by the knee adduction moment. While knee osteoarthritis gait research focuses on this variable, emerging evidence supports a critical role for knee kinematics, muscle activation patterns and the kinematics and kinetics of other lower extremity joints in the development and progression of this disease. Nevertheless, abnormal knee loading is not the only cause of articular cartilage disruption. Excessive and repetitive loading, together creating a total exposure to loading, are critical factors in knee pathomechanics. To assess excessive and repetitive loading, cumulative load is a biomechanical approach that integrates loading exposures to represent the accumulated load that knee tissues endure during physical activity. SUMMARY: Knee osteoarthritis pathomechanics involves an interaction between abnormal and excessive and/or repetitive loading. Mechanics of lower extremity joints and muscle activation patterns influence the knee loading environment. Future work could integrate measures of abnormal loading with assessments of the total exposure to loading during physical activity to better link biomechanics with clinical outcomes in knee osteoarthritis.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".