Prevalnace of Knee Pain in Chronic Stroke Patients with Weight Bearing Asymmetry
Bibliographic record
Abstract
Background : Stroke is sudden loss of neurological function caused by an interruption of the blood flow to the brain. Stroke survivors experience a long-term balance and mobility problems. Generally the nonparetic limb bears more weight than the paretic limb; this is known as weight bearing asymmetry. Walkingdysfunction is most commonly reported limitation after stroke and can markedly affect independence,quality of life, and participation. As a resut of weight bearing asymmetry and persisting stroke-related gaitdeviations there can be development of secondary musculoskeletal complications.Materials & Methodology : A cross-sectional study with stroke (n=100) patient was done in duration of 6months. Patient with pain in knee before stroke, hemispatial neglect, who couldn’t follow verbal commands,unable to stand independently, were excluded. Weight bearing asymmetry was checked with the help of 2weighing scales. Pain was assessed with the help of McGill pain questionnaire.Results : It was found that mean age of patients was 52.89 years, mean height was 1.67, mean weight was65.18kgs, and mean BMI was 23.45 (normal). Mean time since stroke was 24.21 months, mean weight onright lower limb was 32.46 kg and mean weight on left lower limb was 32.26 kg and mean McGill pain scorewas 23.40. Correlation analyses revealed a moderate positive relationship between weight and weight onright lower limb (r=-0.51, p<0.01) also weight and weight on left lower limb (r=-0.57, p<0.01). A moderatenegative relationship between weight on right lower limb and weight on left lower limb (r=--0.41, p<0.01).Finally, it was found that out of 100 chronic stroke patients, 48 patients had knee pain in non-paretic limbwith weight bearing asymmetry.Conclusion : There is impact on knee joint in chronic stroke patient with weight bearing asymmetry.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".