Falls among community-residing stroke survivors following inpatient rehabilitation: a descriptive analysis of longitudinal data
Bibliographic record
Abstract
BACKGROUND: Stroke victims are at relatively high risk for injurious falls. The purpose of this study was to document longitudinal fall patterns following inpatient rehabilitation for first-time stroke survivors. METHODS: Participants (n = 231) were recruited at the end of their rehab stay and interviewed monthly via telephone for 1 to 32 months regarding fall incidents. Analyses were conducted on: total reports of falls by month over time for first-time and repeat fallers, the incidence of falling in any given month; and factors differing between fallers and non fallers. RESULTS: The largest percentage of participants (14%) reported falling in the first month post-discharge. After month five, less than 10% of the sample reported falling, bar months 15 (10.4%) and 23 (13.2%). From months one to nine, the percentage of those reporting one fall with and without a prior fall were similar. After month nine, the number of individuals who reported a single fall with a fall history was twice as high compared to those without a prior fall who reported falling. In both cases the percentages were small. A very small subset of the population emerged who fell multiple times each month, most of whom had a prior fall history. At least a third of the sample reported a loss of balance each month. Few factors differed significantly between fallers and non-fallers in months one to six. CONCLUSION: Longitudinal data suggest that falls most likely linked to first time strokes occur in the first six months post discharge, particularly month one. Data routinely available at discharge does not distinguish fallers from non-fallers. Once a fall incident has occurred however, preventive intervention is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".