CHRONIC MUSCULOSKELETAL PAIN DIMINISHES THE MULTISCALE COMPLEXITY OF STANDING POSTURAL CONTROL IN OLDER ADULTS
Bibliographic record
Abstract
Chronic musculoskeletal pain (CMP) contributes to increased fall risk among older adults. Fall often occurs due to loss of standing balance. Standing postural control is “complex”, depending upon numerous inputs interacting across multiple temporal-spatial scales. The decreased complexity has been linked to increased fall risk. Pain may interfere with this metric of postural control, thus, lead to falls. The aim of this study is to determine whether CMP is associated with sway complexity in older adults. In the MOBILIZE Boston Study, 738 community-dwelling adults aged ≥70y completed a functional assessment, including eyes-open standing postural sway on a force plate. The degree of sway complexity was quantified using multiscale entropy. The Brief Pain Inventory and McGill Pain Map assessed global pain severity and pain locations, respectively. Relationships between pain and sway complexity were analyzed using multivariable Generalized Linear Models. More severe pain was significantly associated with peripheral neuropathy, impaired processing speed (Trail Making A (TMT-A)) and global cognitive function (Mini-Mental State Examination (MMSE). Those with moderate-to-severe pain had lower complexity compared to those with mild or no pain (p=0.01), adjusted for age, gender, education, neuropathy, and TMT-A or MMSE. Participants with lower leg pain had lower sway complexity than those without (p=0.01); no association between pain in other body sites and sway complexity were observed. CMP severity and lower leg pain are associated with decreased sway complexity in older adults. Future studies are needed to explore the underlying mechanisms through which the CMP affects sway complexity, thus, contributes to falls.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".