Pain Factors Associated With Physical Disability in a Sample of Community-Dwelling Senior Citizens
Bibliographic record
Abstract
BACKGROUND: Little is known about the specific aspects of pain that may contribute to the association between pain and disability. This study investigated whether the presence of a physical disability is associated with specific aspects of musculoskeletal pain. METHODS: Questionnaires sent to a sample of community-dwelling seniors included detailed questions about pain; the topics covered pain intensity, frequency, duration and location, use of pain medication, cause of pain, physical disability, depressive symptoms, chronic conditions, and demographic information. RESULTS: Of the 885 respondents, 644 reported musculoskeletal pain (mean age = 75.85 years, SD = 5.83; 63.2% men vs 36.8% women). Multiple logistic regression analysis revealed that pain of severe or greater intensity was shown to be significantly associated with disability (odds ratio [OR] = 4.32, 95% confidence interval [CI] 2.01 and 9.01, respectively). Pain experienced all or nearly all of the time (OR = 2.00, 95% CI 1.07 and 3.72) and taking pain medication (OR = 1.64, 95% CI 1.08 and 2.5 1) were also shown to be associated with disability. The number of pain locations reported by the respondents was also shown to be significantly associated with disability. The OR for the mean number of pain locations (5.8 locations out of a possible 45) was calculated to be 2.12 (95% CI 1.43 and 3.16). CONCLUSION: A thorough pain evaluation and appropriate management of certain aspects of pain may aid in the independent functioning of elderly persons.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".