Effects of eccentric muscle energy technique versus static stretching exercises in the management of cervical dysfunction in upper cross syndrome: a randomized control trial
Bibliographic record
Abstract
OBJECTIVE: To compare the effects of eccentric muscle energy technique versus static stretching exercises combined with cervical segmental mobilisation in the management of upper cross syndrome in patients having neck pain. METHODS: The randomised controlled trial was conducted at the Khan Kinetic Treatment Canada Orthopaedic and Rehabilitation Centre, Rawalpindi, Pakistan, from August 2017 to January 2018, and comprised patients of upper cross syndrome who were randomized into two equal groups using lottery method. Patients in Group-A were treated with eccentric muscle energy technique with cervical segmental mobilisation, while those in Group-B received static stretching exercises with cervical segmental mobilisation. Two sessions per week for 3 weeks were given to each patient who were analysed by measuring tragus-to-wall distance, visual analogue scale and neck disability index. Cervical passive range of motion was measured using inclinometer. Data was recorded at baseline and after 3 weeks of treatment. Data was analysed using SPSS 21. RESULTS: Of the 40 subjects, 20(50%) each were in the two groups. In Group-A mean age was 42.75±11.13 years. In Group-B, it was 40.50±9.14 years. Eccentric muscle energy technique and static stretching technique both showed significant results (p<0.05) for within group analysis, but comparison across groups showed non-significant results (p>0.05 each) on all parameters. CONCLUSIONS: Both the techniques used were found to be equally effective in decreasing pain, improving cervical range of motion and reducing neck disability.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".