Modifiable Risk Factors Identify People Who Transition from Non-fallers to Fallers in Community-Dwelling Older Adults: A Prospective Study
Bibliographic record
Abstract
PURPOSE: To identify modifiable risk factors associated with the transition from non-faller to faller in community-dwelling older adults. METHOD: A prospective study design was used. Adults aged 60 to 90 years (n=90, mean age=79.7 years, 63% male) who did not report falling in the past year were included. A comprehensive geriatric assessment was performed at study baseline, and daily falls data were collected monthly for 1 year. Multivariable regression using a modified Poisson model on fall status (yes/no) and a Cox proportional hazards model for time to first fall were used to identify risk factors. RESULTS: Twenty-four people (27%) fell. Modifiable risk factors were present in 67% of study participants, and fall risk increased as the number of risk factors increased. The most common activities performed prior to falling were walking and using stairs. Fall risk doubled ([relative risk=2.00; 95%CI: 1.13-3.56) per unit increase in the number of risk factors (lower-extremity weakness, balance impairment, and ≥4 prescription medications). CONCLUSIONS: Among older adults who were self-reported non-fallers, falls were a common outcome, and modifiable risk factors were present in the majority of the sample. The absence of a fall history does not rule out the need to screen for other risk factors for falls. Functional lower-extremity weakness, balance impairment as measured by the Berg Balance Scale (score <50), and number of risk factors were independent predictors for the transition in status from non-faller to faller. Further research is required to define effective interventions to prevent first 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".