EFFECT OF SCIATIC NERVE MOBILIZATION ON LOWER LIMB FUNCTION AMONG SUBJECTS WITH DIABETIC NEUROPATHY
Bibliographic record
Abstract
The most frequent consequence that diabetes people experience is diabetic neuropathy. The form of diabetic neuropathy that occurs most frequently is distal symmetrical polyneuropathy, involving sensory, motor and autonomic fibers. Sensory disturbance is most common and may be seen as hypersensitivity to touch, burning sensation, tingling and pricking pain sensation. Techniques for nerve mobility can be utilized to lessen discomfort and enhance sensibility in the area that the nerve feeds. The goal of this study is to evaluate the impact of sciatic nerve mobilization on the performance of the lower limbs and the degree of peripheral neuropathy in diabetic neuropathy patients. A Quasi-experimental design was carried out in Saveetha physiotherapy OPD, Saveetha medical college and hospital, Chennai involving 30 patients recruited using convenient sampling procedure. The data were tabulated and analyzed using Graph pad prism. This study focuses on the Influence of sciatic nerve mobilization on lower limb mobilization among subjects with diabetic neuropathy. Key Words: Sciatic nerve mobilization, Diabetic neuropathy, Lower limb function, Dynamic gait index scale, Toronto clinical neuropathy score.
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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".