Study of Kinesiophobia in Patients with Shoulder Pain
Bibliographic record
Abstract
Background: Kinesiophobia has been established as an important factor among the patients with musculoskeletal pain. Thus the study aims to explore prevalence of kinesiophobia among patient with shoulder pain and also to find out the correlation between age and kinesiophobia and pain and kinesiophobia. Aims and Objectives: To find out the prevalence of kinesiophobia among the patients having shoulder pain. To find out correlation between kinesiophobia and age. To find out the correlation between pain and kinesiophobia Methodology: A study with 50 subjects of age group between 40 to70 patients suffering from acute, subacute and chronic shoulder pain were selected. Pain was measured using NPRS and subjects were assessed using the tampa scale of kinesiophobia in which the scores above 37 were considered to have positive kinesiophobia whereas the score below were considered negative. Result- Positive kinesiophobia was present in 40 patients out of 50 that is 80% .This study also shows positive correlation between age and kinesiophobia with significant p value and also positive correlation between pain and TSK score. Conclusion: The study concludes that positive kinesiophobia is strongly associated with majority of the older adult patient with acute, subacute and chronic shoulder pain. Also with increasing age patients developed more severe kinesiophobia. Patient associated with high intensity of pain have higher tampa score. Key words: kinesiophobia, shoulder pain, Tampa scale.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".