A comparison of pain and disability scores in different grades of knee OA before and after low level laser therapy (LLLT)
Bibliographic record
Abstract
Osteoarthritis of knee (KOA) is the most common cause of morbidity in the elderly. As the knee joint is involved in weight- bearing, aging causes wear and tear of cartilages in the knee joint resulting in degenerative changes. Kellgren-Lawrence grading of knee OA are described according to the findings noted on the X-ray of Knee Joint. Low-level laser therapy in the management of knee osteoarthritis. The assessment of pain and disability in subjects with grade II and grade III KOA before and after the low-level laser therapy was done using the questionnaires. Forty-five Subjects who are symptomatic and who had radiological criteria based on Kellgren-Lawrence (KL) grade II and III were included in the study. Visual analog scale (VAS), Western Ontario and McMaster Universities Arthritis Index (WOMAC) and Lequesne index of pain and disability was used before and after low level laser therapy in the subjects of the study. There was no statistical difference between the grade II and grade III KOA for VAS, WOMAC scores. However, there was statistically significant difference in Lequesne index scores between the grade III KOA participants (p = 0.027) not for grade II KOA. Lequesne index had a smaller number of question when compared to WOMAC and more specific questions when compared to VAS. The questionnaire is specific to assessment of progression of functional status of the individual in grade III knee OA.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".