Comparison of Effectiveness of Interferential Therapy and Transcutaneous Electrical Nerve Stimulation in Construction Workers having Cervical Spondylosis Using McGill Pain Questionnaire
Bibliographic record
Abstract
Background: Degeneration can occur as a result of moisture loss and decreased flexibility in the neck’s spinal discsover time. Cervical spondylosis can also be brought in addition to recurrent neck motions, prior neck injuries, badposture, and genetic factors and by poor posture.Purpose: The study objective was comparison of the efficiency of interferential therapy and transcutaneouselectrical nerve stimulation in construction workers having cervical spondylosis using the short form of the MCGill pain questionnaire.Methodology: 50 subjects participated in the study from Mars India Builds and selected based on the inclusion andexclusion criteria. Subjects assigned into interferential therapy group (n=25) and transcutaneous electrical nervestimulation group (n=25). Along with this, static neck exercises were given to both groups. The treatment periodwas given for 40 mins and 6 days per week and continued for 2 weeks. The entire study process is conducted fromNovember 2022 to April 2023.Result: From the finding of this study interferential therapy group post-test mean was 8.88 and whereas thetranscutaneous electrical nerve stimulation group was 11.72. This strongly suggests that interferential therapyin construction workers having cervical spondylosis along with static neck exercises is more effective thantranscutaneous electrical nerve stimulation.Conclusion: In this study interferential therapy with static neck exercises among construction workers was foundto be more effective than transcutaneous electrical nerve stimulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".