Efeitos do sexo, da severidade e da velocidade nos parâmetros espaço-temporais e angulares e na variabilidade dos parâmetros angulares da marcha em pessoas com osteoartrite de joelho
Bibliographic record
Abstract
Osteoarthritis (AO) is the most common musculoskeletal disease in the world and the knee is the most affected joint. Women have twice the risk of developing knee osteoarthritis and the effect that sex and severity of knee osteoarthritis (OAJ) would have on spatiotemporal parameters and angular variability, and their relationship with clinical measures are still uncertain. The aim of this study was to investigate the effect that sex and severity of OAJ would have on spatiotemporal parameters and angular variability during gait at different speeds, and to correlate them with muscle strength and the perception of pain and physical function. Forty-two OAJ individuals and 19 healthy people underwent the application of the visual analogue pain scale (VAS) and Western Ontario and McMaster Universities (WOMAC), evaluation of muscle strength and kinematics of gait on a treadmill at three speeds: comfortable, 20% higher and 20% lower than the comfortable speed. The results revealed that there was no effect of sex or severity on the range of motion of lower limb joints or on angular variability in the sagittal plane. However, there was some effect of severity on clinical measures (EVA and WOMAC) and on muscle strength assessment, but there was an effect of sex on these measures and on step length. Women had shorter stride length, greater stiffness, worse physical function and less muscle strength than men, with a significant correlation between these variables, indicating that the focus on rehabilitation should be on reducing pain and increasing muscle strength and joint mobility.
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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 | 0.005 |
| 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) | 0.002 | 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".