Evaluation of polyherbal gel for musculoskeletal injuries in industrial workers
Bibliographic record
Abstract
BACKGROUND: Industrial workers often have musculoskeletal disorders due to the nature of their work. OBJECTIVE: The goal was to investigate the scientific use of polyherbal gel in relieving pain and stiffness due to musculoskeletal injuries and improving activities of daily living (ADLs) in industrial workers. METHODS: A pragmatic, single-blinded, randomized control study divided 200 musculoskeletal injury patients into four parallel groups (n = 50). Groups 1 and 2 were applied polyherbal gel via phonophoresis with therapeutic ultrasound and superficial massage. Groups 3 and 4 received diclofenac diethyl-ammonium 1% gel by phonophoresis and superficial massage. The Global Pain Relief Scale, Numeric Pain Rating Scale (NPRS), and Western Ontario and McMaster Universities Arthritis Index (WOMAC) were used to measure pain, stiffness, and ADLs. Data was analyzed using one-way analysis of variance (ANOVA) and paired t-test to compare mean±SD of four independent groups before and after gel application. The confidence interval was 95%, with p < 0.05 considered significant. RESULTS: The results revealed that polyherbal gel reduced pain (NPRS, WOMAC and Global pain relief scales) more efficiently (p≤0.000) when applied with phonophoresis as compared to applied with massage and standard diclofenac (p≤0.005), furthermore, polyherbal gel when applied with phonophoresis showed more efficient results. CONCLUSION: Industrial workers with musculoskeletal injuries benefited from the use of polyherbal gel for pain and inflammation relief. The polyherbal gel is natural, cost-effective, and easy to formulate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".