Pain Impact on the Daily Life Activities of Patients with Musculoskeletal Disorders
Bibliographic record
Abstract
Objective This study is aimed at assessing the negative impact of pain on daily life activities of patients with muscu-loskeletal disorders(MSDs).[Methods]The adverse effects of pain due to MSDs on daily life activities were assessed using McGill's questionnaire.The study population included558patients with MSDs.[Results] the prevalence of pain associated with MSDs was99.5%,with an average score of6.98±1.62by Visual Analogue Scale(VAS).The intensity of the pain was associated with age(F=7.82,P0.01)but not significantly with gender(t=-1.9091,P0.05).The logistic regression analysis indicated that the nature and severity of the pain,and its effect on the mood states of patients are important factors affecting the daily life.Furthermore,women and elderly patients seemto be the subpopulation more susceptible to the pain.[Conclusion]Pain associated with MSDs has obviously shown its negative impact on the daily life of the patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".