Effectiveness of Client Centered- ADL Training Program for Improving Upper Extremity Function in Sub-acute Adhesive Capsulitis: A Randomized Controlled Trial
Bibliographic record
Abstract
CONTEXT: Adhesive Capsulitis has been described as a self-limiting condition, lasting on an average 2-3 years. It is estimated to affect 2% -5% of the population. Usually affects performance in activities of daily living. AIM: To study the effectiveness of Actual training of ADL’s along with Conventional Occupational Therapy in improving Upper Extremity function in Sub-acute stage of Adhesive capsulitis METHODS AND MATERIALS: 10 Patients between the age 40-60 years of both the genders, with unilateral shoulder pain for more than 3 months and had received only analgesic treatment. STATISTICAL ANALYSIS: Statistical analysis-Pain on VAS, UEFI, Muscle Power, ROM And COPM outcomes were done by using descriptive and inferential statistics using chi-square test, Mann Whitney U test and Wilcoxon Signed Rank Test and software used in the analysis were SPSS 24.0 version and GraphPad Prism 7.0 version and p<0.05 is considered as level of significance. CONCLUSION: On therapeutic basis in Occupational Therapy, the Conventional Occupational therapy and Actual ADL training both were effective in subacute stage of Adhesive Capsulitis. But, when Client - Centered care perspective considered, then Actual ADL training along with Conventional Occupational therapy is found to be effective than Conventional Occupational therapy alone. Key words: ADL (Activities of Daily Living), COPM (Canadian Occupational Performance Measure), ROM (Range of Motion), UEFI (Upper Extremity Function Index)
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".