BIOLOGICAL MARKERS OF FALLS IN OLDER ADULTS WITH MILD COGNITIVE IMPAIRMENT
Bibliographic record
Abstract
Abstract Mild Cognitive Impairment (MCI) nearly doubles the risk of falls in older adults. While the association between cognition and falls risk is relatively well understood, no study has explored how biological markers of organic integrity contribute to falls risk in MCI. This study aimed to identify biological markers that could be associated with an increased rate of incidental falls in MCI. A total of 142 participants with MCI (aged 74.3 ± 6.1 years;75% women) with blood tests performed at baseline in Gait and Brain Cohort Study were analyzed. Participants were followed up to 7 years (mean of 28 ± 22.1 months), and falls were reported during the follow-up period(70% fell). Eighteen biological markers extracted from blood serum were dichotomized into low and moderate-to-high levels based on tertile cut-offs. Negative binomial regression model was used to estimate the incident rate ratio (IRR) of falls for biological markers, adjusted for age, sex, global cognition, gait speed, total follow-up duration, and history of falls in the past 12 months at baseline. Results showed that low Parathyroid Hormone (PTH) level (IRR=1.73; 95%CI=1.07, 2.79; p=0.02) and moderate-to-high Interleukine-8 (IL-8) level (IRR= 1.81; 95%CI 1.05-3.14; p=0.03) were associated with an increased rate of falls during the follow-up. Moderate-to-high IL-8 level was also associated with an increased rate of falls with injuries (IRR=2.07; 95%CI=1.05, 4.08; p=0.03). This suggests that in addition to cognitive deficits, impaired calcium metabolism caused by low PTH level and inflammatory processes affecting internal organs may increase falls risk in older adults with MCI.
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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 | 0.000 | 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".