A COMPARATIVE STUDY ON THE EFFECT OF SELF SNAGs VERSUS DYNAMIC ISOMETRIC EXERCISES IN DESK JOB PEOPLE WITH CHRONIC NECK PAIN
Bibliographic record
Abstract
Background: Neck pain is a common global problem, an important source of disability Bad posture of neck with respect to chest is one of the commonest causes of chronic neck pain. This is common to the job related (e.g. computer professionals, clerks, bank employees) where the neck is kept in a flexed position for long periods of time. The objectives of the study is to compare the effectiveness of self SNAGs with dynamic isometric exercise in chronic neck pain.Methods: It is a comparative experimental study. This study includes (N=40) desk job people with chronic neck pain subjects with age group of 25-45 years. They were randomly assigned into 2 groups (Group A and B). Group A had 20 (N=20) subjects who are treated with self SNAGs, Group B had 20 (N=20) who are treated with dynamic isometric exercise using resistance band. The subjects were given intervention once a day for 6 weeks.Results: At the end of 6 weeks, both neck pain and disability decreased in both groups, range of motion had also improved statistically significantly in both groups, but the results showed that the pain scores are significantly improved in group A( t = -2.707) which is significant at 5% level of significance (p = 0.01). It has been inferred that VAS decreases more when self SNAGs was applied and the difference of means of NDI, t = -4.468 which is highly significant (p = 0.00) implying that NDI decrease more when self SNAGs was applied as compared to dynamic isometric exercise.Conclusion: From the above study it is concluded that, self SNAGs is an effective way of decreasing pain and increasing functional ability when compared to dynamic isometric exercise using resistance band.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".