Slow Processing Speed Predicts Falls in Older Adults With a Falls History: 1‐Year Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: A previous fall is a strong predictor of future falls. Recent epidemiologic data suggest that deficits in processing speed predict future injurious falls. Our primary objective was to determine a parsimonious predictive model of future falls among older adults who experienced ≥1 fall in the past 12 months based on the following categories: counts of (1) total, (2) indoor, (3) outdoor or (4) non-injurious falls; (5) one mild or severe injury fall (yes vs no); (6) an injurious instead of a non-injurious fall; and (7) an outdoor instead of an indoor fall. DESIGN: 12-month prospective cohort study. SETTING: Vancouver Falls Prevention Clinic, Canada (www.fallsclinic.ca). PARTICIPANTS: Two-hundred and eighty-eight community-dwelling older adults aged ≥70 years with a history of ≥1 fall resulting in medical attention in the previous 12 months. MEASUREMENTS: We employed principal component analysis to reduce the baseline predictor variables to a smaller set of five factors (i.e., processing speed, working memory, emotional functioning, physical functioning and body composition/fall risk profile). Second, we used the extracted five factors as predictors in regression models predicting the incidence of falls over a 12-month prospective observation period. We conducted regression analyses for the seven falls-related categories (defined above). RESULTS: Among older adults with a falls history, processing speed was the most consistent predictor of future falls; poorer processing speed predicted a greater number of total, indoor, outdoor, and non-injurious falls, and a greater likelihood of experiencing at least one mild or severe injurious fall (all P values < .01). CONCLUSION: Poorer performance on the processing speed factor, a trainable factor, was independently associated with the most costly type of falls-injurious 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".