The Efficacy of Wetcupping in Kateegraha (Low Back Pain)
Bibliographic record
Abstract
Among musculoskeletal disorders, low back pain (LBP) has the highest prevalence worldwide and is the primary cause of disability. This is the condition that has the highest potential for rehabilitation benefits Chronic LBP is a major cause of work loss and participation restriction and reduced quality of life around the world. In Ayurveda low back pain is correlated with Kateegraha. Shringa avcharana is one of the methods of Raktamokshana and this can be correlated with Chinese cupping method. The suction through specific cupped instrument was used since prehistoric time for the treatment of disease Objective: This case series aims to show how wet cupping affects lower back pain (Kateegraha). Intervention: five patients who complained of low back pain were included to receive four sittings of wet cupping. Cups were applied on the lumbosacral region once every 7 days for 21 days. The visual analogue scale (VAS) and The Quebec Back Pain Disability Scale was used to evaluate the pain on 7 days, 14 days, and 21 days. Conclusion: It's been demonstrated that wet cupping works well for relieving Kateegraha (low back pain). So, we can conclude that for individuals with low back pain, wet cupping is a successful, safe, practical, and reasonably priced treatment plan.
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".