The Trajectories of the Number of Pain Sites and Their Associated Factors in Older Adults: Results from the Korean Longitudinal Study of Ageing
Bibliographic record
Abstract
BACKGROUND: The aging of the population will result in an increase in demand for pain management. Pain adversely affects physical and mental functioning in older adults and accounts for a considerable proportion of all medical expenses. OBJECTIVES: This study was performed to investigate the patterns of changes in the trajectories of the number of pain sites in older adults and the factors that affect these patterns according to gender. METHODS: Data were extracted for subjects that participated in the Korean Longitudinal Study of Ageing from 2006 to 2014. The study population consisted of 2,839 individuals (1,190 men and 1,649 women) ≥60 years old. A group-based trajectory model was used to determine the optimal number of subgroups and the trajectories of the number of pain sites according to gender. A multinomial regression analysis was conducted to identify factors that affect the probability of inclusion in each trajectory group. RESULTS: The trajectories of the number of pain sites were consistent in both genders. Almost all women had one or more pain symptom, and a greater number of pain sites than men. Older age, longest-duration occupation requiring manual labor, lack of physical activity, depressive symptoms, and poor self-rated health were associated with a greater number of pain sites in both genders. A lower level of education, married status, and experience of injury were associated with the number of pain sites in men but not in women, while household income and chronic diseases were associated with the number of pain sites only in women. CONCLUSIONS: The pain status at the early stage is predictive of future pain. In this study, we identified gender differences in the trends of the number of pain sites and associated factors. Further comprehensive studies on pain intensity and duration are required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".