COMPARATIVE EFFECT OF SPENCER TECHNIQUE VS MULLIGAN MOBILIZATION ON ROM AND FUNCTIONAL DISABILITY AMONG ADHESIVE CAPSULITIS PATIENTS
Bibliographic record
Abstract
Background: Adhesive capsulitis characterized by pain and limited ROM and functional disability. Manual therapy techniques such as Spencer and Mulligan promote shoulder function by increasing of ROM and reliving pain. Objective: To find effect of spencer technique vs mulligan mobilization on rom and functional disability among adhesive capsulitis patients Methods: A randomized clinical trial was conducted 38 frozen shoulder subjects. Data was gathered from Mian Munshi DHQ Teaching hospital ,Govt. THQ Mian Meer hospital Lahore on this basis of inclusion and exclusion criteria.2 intervention groups were made .Group A participants undergone spencer technique and group B received Mulligan Mobilization SPADI questionnaire utilized to find out functional disability and goniometer utilized to find ROM were utilized as assessment tool. Study ran April 2024 October 2024. SPSS version 22 employed for statistical analysis. Results: Revealed group B demonstrated superior outcomes compared to Group A, with reductions in SPADI pain in the QoL score for all three domains: physical health (p = .006, disability (p = .033), and total scores p = .007. Showed within-group comparisons has significant reduction in pain, disability and total scores for both groups, more so Group B (p < .001). For shoulder ROM post-intervention Group B reported significantly improved flexion, extension, abduction, internal, and external rotations (p < .05) than Group A. Conclusion: Conclusively both groups benefited in all parameters Although; Group B Mulligan Mobilization was more effective in pain and disability reduction and ROM improvement as compared to group B spencer technique.
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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) | 0.001 | 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) | 0.005 | 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".