Comparing the causes, circumstances and consequences of falls across mobility statuses among individuals with spinal cord injury: A secondary analysis
Bibliographic record
Abstract
OBJECTIVE: To compare the occurrence of falls and fall-related injuries, and the circumstances of falls among individuals with spinal cord injury (SCI) who ambulate full-time, use a wheelchair full-time and ambulate part-time. DESIGN: A secondary analysis. SETTING: Community. PARTICIPANTS: Adults with SCI. INTERVENTION: None. OUTCOME MEASURES: The occurrence and circumstances of falls and fall-related injuries were tracked over six-months using a survey. Participants were grouped by mobility and fall status. A chi-square test compared the occurrence of falls and fall-related injuries, and the time and location of falls, and a negative binomial regression was used to predict the likelihood of falls by mobility status. Kaplan-Meier analysis was used to determine differences in the time to first fall based on mobility status. Group characteristics and causes of falls were described. RESULTS: = 8) were analyzed. Mobility status was a significant predictor of falls (P < 0.01); individuals who used a wheelchair full-time had a third of the likelihood of falling than those who ambulated full-time (P < 0.01). Type of fall-related injuries differed by mobility status. Those who ambulated full-time fell more in the daytime (P < 0.01). Individuals who ambulated full-time and part-time commonly fell while walking due to poor balance, and their legs giving out, respectively. Those who used a wheelchair full-time typically fell while transferring when rushed. CONCLUSION: Mobility status influences the likelihood and circumstances of falls. Mobility status should be considered when planning fall prevention education/training for individuals with SCI.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| 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) | 0.003 | 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".